Method and tools for prognosis of cancer in her2+partients

ABSTRACT

A gene or protein set includes at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, and possibly 40, 45, 50, 55, 60, 65 genes or proteins, antibodies or hypervariable portion thereof directed against the proteins encoded by these genes.

FIELD OF THE INVENTION

The present invention is related to methods and tools for obtaining an efficient prognosis (prognostic) of cancer HER2+ patients wherein tumor invasion related genes are the keys player of breast cancer prognosis.

BACKGROUND OF THE INVENTION

Breast cancer and especially invasive ductal carcinoma is the most common cancer in women in Western countries. Several prognostic signatures based on genetic profiling have been established. These different signatures all reflect the capacity of the tumor cells to proliferate¹. Their use permit to distinguish tumors with low and high proliferative activity, respectively the luminal A tumors characterized by a low proliferation rate and associated with good prognosis (prognostic) and a second group comprising the basal-like, HER2 (ERBB2) and luminal B tumors with high proliferation rate and associated with bad prognosis (prognostic).

Several studies have been realized about the role of the adaptive immune response in controlling the growth and recurrence of human tumors. In human colorectal cancer, it was shown that in situ analysis of tumor-infiltrating immune cells may be a valuable prognostic tool². Bates and al. showed that quantification of FOXP3-positive TR in breast tumors is valuable for assessing disease prognosis (prognostic) and progression³. Therefore, it exist a need to investigate biological processes that trigger breast cancer progression and that depend on a specific molecular subtype and a need to investigate the immune cells in breast cancer using human breast cancer model, especially CD4+ cells which regulate the immune response.

CD4+ cells belong to the leukocyte family which is a major component of the breast tumor microenvironment. CD4 marker is mainly expressed on helper T cells and with a limited level on monocyte/macrophages and dendritic cells. Immune cells play a role in tumor growth and spread, notably in breast tumor, and CD4+ cells are key players in the regulation of immune response.

Furthermore it is known that prognosis (prognostic) and management of breast cancer has always been influenced by the classic variables such as histological type and grade, tumor size, lymph node involvement, and the status of hormonal-estrogen (ER; ESR1) and progesterone receptors- and HER-2 (ERBB2) receptors of the tumor. Recently, different research groups identified several gene expression signatures predicting clinical outcome. A common feature to all these gene expression signatures is that they outperform conventional clinico-pathological criteria mostly by identifying a higher proportion of low-risk patients not necessarily needing additional systemic adjuvant treatment, while still correctly identifying the high-risk patients. Although they are all addressing the same clinical question, it might be surprising that there is only little or none overlap between the different gene lists, raising the question about their biological meaning. Also, although it has repeatedly and consistently been demonstrated that breast cancer, in addition to being a clinically heterogeneous disease, is also molecularly heterogeneous, with subgroups primarily defined by ER (ESR1), HER-2 (ERBB2) expression, the different prognostic signatures were never clearly evaluated and compared in these different molecular subgroups. This was probably due to the relatively small sizes of the individual studies, which would have made these findings statistically unstable.

Epithelial-stromal interactions are known to be important in normal mammary gland development and to play a role in breast carcinogenesis. Therefore, there exists a need to explore the influence of breast tumor microenvironment on primary tumor growth, breast cancer sub-typing and metastasis.

Therefore, it exists especially a need to investigate the biological processes and tumor markers that are involved in specific molecular subtype that do not belong to the status of the hormonal-estrogen (ER; ESR1) receptor, especially to investigate the biological process and tumor marker that are involved in the HER-2 (ERBB2) receptor molecular subtype.

AIMS OF THE INVENTION

The present invention aims to provide methods and tools that could be used for improving the diagnosis (diagnostic) especially the prognosis (prognostic) of tumors, preferably breast tumors, especially in patient identified as HER2+/ERBB2 patients, in addition to the identification of patients identified as ER+ (ESR1+ patients) and/or ER− patients wherein immune response is the key player for cancer prognosis.

The present invention aims to provide methods and tools which improved the prognosis (prognostic) of patient and do not present drawbacks of the state of the art but also are able to propose a prognostic of all patients presenting a predisposition to tumors especially breast tumors development, which means patients which are identified as HER2+/ERBB2 patients, but also ER+ patients and ER-patients.

SUMMARY OF THE INVENTION

The present invention is related to gene/protein set (or library) that is selected from mammal (preferably human) tumor invasion associated (or related) genes and proteins which are used for the prognosis (prognostic, detection, staging, predicting, occurrence, stage of aggressiveness, monitoring, prediction and possibly prevention) of cancer in HER2+ patients.

A first aspect of the present invention is related to a gene or protein set comprising or consisting of at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35 and possibly 40, 45, 50, 55, 60, 65 genes or proteins or the entire (gene) set selected from the table 12 and/or table 13 and (preferably monoclonal) antibodies (or hypervariable portion thereof) specifically directed against their encoded proteins sequences.

Advantageously, the gene and protein set according to the invention were selected from the gene and protein (including antibodies or their hypervariable portion thereof) that are bound to a solid support surface preferably according to an array.

The present invention is also related to a diagnostic kit or device comprising the gene or protein set according to the invention possibly fixed upon a solid support surface according to an array and possibly other means for real time PCR analysis (by suitable primers which allows a specific amplification of 1 or more of these genes selected from the gene set) or protein analysis.

The solid support could be selected from the group consisting of nylon membrane, nitrocellulose membrane, polyvinylidene difluoride, glass slide, glass beads, polyustyrene plates, membranes on glass support, CD or DVD surface, silicon chip or gold chip.

Preferably, set means for real time PCR analysis are means for qRT-PCR of the genes of the gene set (especially expression analysis (over or under expression) of these genes).

Another aspect of the present invention is related to a micro-array comprising one or more genes or proteins selected from the gene or protein set according to the invention, possibly combined with other genes or proteins selected from other genes or proteins sets for an efficient diagnosis (diagnostic) preferably prognosis (prognostic) of tumors, preferably breast tumors.

Another aspect of the present invention is related to a kit or device which is preferably a computerized system, comprising

a bio assay module configured for detecting gene expression (or protein synthesis) from a tumor sample, preferably based upon the gene or protein set according to the invention and

a processor module configured to calculate expression (over or under expression) of these genes (or synthesis of corresponding encoded proteins) and to generate a risk assessment for the tumor sample (risk assessment to develop a malignant tumor).

Preferably, the tumor sample is any type of tissue or cell sample obtained from a subject presenting a predisposition or a susceptibility to a tumor, preferably a breast tumor, that could be collected (extracted) from the subject. The subject could be any mammal subject, preferably a human patient and the sample could be obtained from tissues which are selected from the group consisting of breast cancer, colon cancer, lung cancer, prostate cancer, hepatocellular cancer, gastric cancer, pancreatic cancer, cervical cancer, ovarian cancer, liver cancer, bladder cancer, cancer of the urinary track, thyroid cancer, renal cancer, carcinoma, melanoma or brain cancer preferably, the tumor sample is a breast tumor sample.

Advantageously, the gene or protein set according to the invention could be combined, preferably in a diagnostic kit or device with other genes or proteins selected from other gene or protein sets preferably the gene or protein set(s) comprising or consisting of at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95 and possibly 100, 105, 110 or the entire set selected from table 10 and/or table 11 or antibodies and hypervariable portion thereof directed against their encoded proteins for an efficient prognosis (prognostic) of other types of breast cancer (ER−, breast cancer type)(possibly combined with one or more gene of the set of genes as described by A. Teschendorff et al (genome biology nr 8,R157-2007 dedicated to efficient prognostic of cancer of ER− patient).

According to another embodiment of the present invention, the gene or protein set according to the invention comprises or consists of at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95 genes or the entire set selected from the genes designated as upregulated genes in grade 3 tumors in the table 3 of the document WO 2006/119593 or antibodies directed against the corresponding encoded proteins. Preferably, these genes are proliferation related genes, preferably the gene set comprises at least the 8 genes selected from the group consisting of CCNB1, CCNA2, CDC2, CDC20, MCM2, MYBL2, KPNA2 and STK6.

Preferably, the selected genes/proteins are the 4 following genes/proteins: CCNB1, CDC2, CDC20 and MCM2 or more preferably CDC2, CDC20, MYBL2 and KPNA2 as described in the U.S. CIP patent application Ser. No. 11/929,043. These genes/proteins sequences are advantageously bound to a solid support as an array.

These genes/proteins present in a (diagnostic) kit or device may also further comprise means for real time PCR analysis of these preferred genes, preferably these means for real time PCR are means for qRT-PCR and comprise at least 8 sequences of the primers sequences SEQ ID NO 1 to SEQ ID NO 16.

Furthermore, these gene/protein sets may also further comprise reference genes/proteins, preferably 4 references genes for real time PCR analysis, which are preferably selected from the group consisting of the genes TFRC, GUS, RPLPO and TBP.

These reference genes are identified by specific primers sequences, preferably the primers sequences selected from the group consisting of SEQ ID NO 17 to SEQ ID NO 24.

With this set of genes, the person skilled in the art may also obtain (calculate) the gene expression grade index (GGI) or relapse score (RS).

The content of this previous PCT patent application (WO 2006/119593 and its CIP application Ser. No. 11/929,043 are incorporated herein by reference.

The person skilled in the art may also select other prognostic means (signatures) or gene/protein lists (gene/protein set which could be used for an efficient prognosis (prognostic) of cancer in ER− and ER+ patients such as the one described by:

-   Wang et al (lancet 365 (9460) p. 671-679 (2005)), -   Van't Veer et al (Nature 415 (6871) p. 530-536 (2002)), -   Paik et al (Engl. J. Med., 351 (27) p. 2817-2826 (2004)), -   Teschendorff (Genome Biol., 7 (10) R101 (2006)), -   Van De Vijver et al (Engl. J. Med. 347 (25) p. 1999-2009 (2002)), -   Perou et al (Nature, 406, p 747-752 (2000)) -   Sotiriou et al, (PNAS 100 (18) p. 8414-8423 (2003)). -   Sorlie et al (STNO—The Stanford/Norway dataset PNAS, 98 (19) p.     10869-10874 (2001)).     http://genome-www.stanford.edu/breast.cancer/mopo.clinical/data.shtml     and the expression profiling proteins used in breast cancer     prognosis as described in the document WO 2005/071419 which     comprises at least one, two, three or more genes or proteins     selected from the group consisting of Afadin, Aurora A, a-Catenin,     b-Catenin, BCL2, Cyclin D1, Cyclin E, Cytokeratin 5/6, Cytokeratin     8/18, E-Cadherin, EGFR, ERBB2, ERBB3, ERBB4, Estrogen receptor,     FGFR1, FHIT, GATA3, Ki67, Mucin 1, P53, P-Cadherin, Progesterone     receptor, TACC1, TACC2, TACC3 and possibly one or more gene or     protein selected from the group consisting of Cytokeratin 6,     Cytokeratin 18, Ang1, AuroraB, BCRP1, CathepsinD, CD10, CD44, CK14,     Cox2, FGF2, GATA4, Hif1a, MMP9, MTA1, NM23, NRG1a, NRG1beta, P27,     Parkin, PLAU, 5100, SCRIBBLE, Smooth Muscle Actin, THBS1, TIMP1.

The person skilled in the art may also select one or more gene used for analysis differential gene expression associated with breast tumor as described in the document WO 2005/021788 especially the sequence of the gene ERBB2, GATA4, CDH15, GRB7, NR1D1, LTA, MAP2, K6, PKM1, PPARBP, PPP1R1B, RPL19, PSB3, LOC148696, NOL3, loc283849, ITGA2B, NFKBIE, PADI2, STAT3, OAS2, CDKL5, STAITGB3, MKI67, PBEF, FADS2, LOX, ITGA2, ESTA1878915/NA, JDPA, NATA, CELSR2, ESTN33243/NA, SCUBE2, ESTH29301/NA, FLJ10193, ESRA and other gene or protein sequence described in the gene set of this PCT patent application.

The kit or device according to the invention may therefore comprise 1, 2, 3 or more gene/protein sets preferably dedicated to each type of patient group (ER-patient group, ER2+ patient group and HER2+ patient group) and could be included in a system which is a computerized system comprising 1, 2 or 3 bio assay modules configured for gene expression (or protein synthesis) of 1 or more of these gene/protein sets for an efficient diagnosis (prognosis) of all types (ER+, ER−, HER2+) of breast cancer. This system advantageously comprises one or more of the selected gene sets of the invention and a processor module configured to calculate a gene expression of this gene set(s) preferably a gene expression grade index (GGI) to generate a risk assessment for a selected tumor sample submitted to a diagnosis (diagnostic).

Advantageously, the molecules of the gene and protein set according to the invention are (directly or indirectly) labelled. Preferably, the label selected from the group consisting of radioactive, colorimetric, enzymatic, bioluminescent, chemoluminescent or fluorescent label for performing a detection, preferably by immunohistochemistry (IHC)analysis or any other methods well known by the person skilled in the art.

The present invention is also related to a method for the prognosis (prognostic) of cancer in a mammal subject preferably in a human patient preferably in at least ER− patient which comprises the step of collecting a tumor sample (preferably a breast tumor sample) from the mammal subject (preferably from the human patient) and measuring gene expression in the tumor sample by putting into contact sequences (especially mRNA sequences) with the gene/protein set according to the invention or the kit or device according to the invention and possibly generating a risk assessment for this tumor sample (preferably by designated the tumor sample as different subtypes within the ER− type and possibly in the ER+ and HER2+ types as being as higher risk and requiring a patient treatment regimen (for example adjusted to a specific chemotherapy treatment or specifically molecular targeted anti cancer therapy (such as immunotherapy or hormonotherapy).

In particular, the invention is also useful for selecting appropriate doses and/or schedule of chemotherapeutics and/or (bio)pharmaceuticals, and/or targeted agents, among which one may cite Aromatase Inhibitors, Anti-estrogens, Taxanes, Antracyclines, CHOP or other drugs like Velcade™, 5-Fluorouracil, Vinblastine, Gemcitabine, Methotrexate, Goserelin, Irinotecan, Thiotepa, Topotecan or Toremifene, anti-EGFR, anti-HER2/neu, anti-VEGF, RTK inhibitor, anti-VEGFR, GRH, anti-EGFR/VEGF, HER2/neu & EGF-R or anti-HER2.

Another aspect of the present invention is related to a method for controlling the efficiency of a treated method or an active compound in cancer therapy. Indeed, the method and tools according to the invention that are applied for an efficient prognosis of cancer in various breast cancer patient types, could be also used for an efficient monitoring of treatment applied to the mammal subject (human patient) suffering from this cancer.

Therefore, another aspect of the present invention is related to a method which comprises the prognosis (prognostic) method according to the invention before (and after) treatment of a mammal subject (human patient) with an efficient compound used in the treatment of subjects (patients) suffering from the diagnosis breast tumor. This means that this method requires a (first) prognosis (prognostic) step which is applied to the patient, before submitting said subject (patient) to a treatment and a (second) diagnosis (diagnostic) step following this treatment.

The inventors use CD10 and/or PLAU signatures according to Tables 12 and/or 13 as diagnosis and/or to assist the choice of suitable medicine.

This method could be applied several times to the mammal subject (human patient) during the treatment or during the monitoring of the treatment several weeks or months after the end of the treatment to reveal if a modification of genes expressions (or proteins synthesis) in a sample subject is obtained following the treatment.

Therefore, another aspect of the present invention is related to a method for a screening of compounds used for their anti tumoral activities upon tumors especially breast tumor, wherein a sufficient amount of the compound(s) is administrated to a mammal subject (preferably a human patient) suffering from cancer and wherein the prognosis (prognostic) method according to the invention is applied to said mammal subject before an administration of said active compound(s) and is applied following administration of said active compound(s) to identify, if the active compound(s) may modify the genetic profile (gene expression or protein synthesis) of the mammal subject.

A modification in the subject (patient) genetic profile (gene expression or protein synthesis) means that the obtained tumor sample before or after administration of the active compound(s) has been modified and will result into a different gene expression (or protein synthesis) in the sample (that is detectable by the gene set according to the invention). Therefore, this method is applied to identify if the active compound is efficient in the treatment of said tumor, especially breast tumor in a mammal subject, especially in a human patient.

Advantageously, in this method the active compound(s) which are submitted to this testing or screening method is recovered and is applied for an efficient treatment of mammal subject (human patient).

DETAILED DESCRIPTION OF THE INVENTION In Vivo Interactions Between Breast Cancer (BC) Cells and Their Stromal Component/Analysis of Alterations in Gene Expressions.

The inventors have adapted the protocol described by Allinen and colleagues (2004) for the isolation of stroma cells and have managed to separate and isolate four different cell subpopulations: tumor epithelial cells (EpCAM positive), leukocytes (CD45 positive), myofibroblasts (CD10 positive) and endothelial cells. The inventors have also tested several RNAs amplification/labeling protocols for our gene expression experiments.

Up today, (myo)fibroblast cells (CD10) were isolated and purified from 28 breast tumors and 4 normal tissues. Gene expression analysis was performed using the Affymetrix GeneChip® Human Genome U133 Plus 2.0 arrays. Survival analysis was carried out using 12 publicly available micro-array datasets including more than 1200 systemically untreated breast cancer patients.

Breast tumor (myo)fibroblast stroma cells showed an altered gene expression patterns to the ones isolated from normal breast tissues (see Tables 12 and 13). While some of the differentially expressed genes are found to be associated with extracellular matrix formation/degradation and angiogenesis, the function of several other genes remains largely unknown.

Unsupervised hierarchical clustering analysis clustered breast tumor (myo)fibroblast cells into four main subgroups recapitulating the molecular portraits of breast cancer based on ER, HER2 status and tumor differentiation.

Similarly to tumor expression profiling studies, BC (myo)fibroblast cells isolated form intermediate grade tumors did not show a distinct gene expression pattern but a mixture of gene expression profiles similar to those derived from well and poorly differentiated tumors respectively.

A stroma gene expression signature developed from (myo)fibroblast cells isolated from normal versus BC tissues showed a statistically significant association with clinical outcome. Breast tumors with high expression levels of the stroma signature were significantly associated with worse prognosis (HR 1.55; CI 1.20-1.99; p=5.57 10⁻⁴). This association was mainly observed within the clinically high risk HER2+ subtypes. Interestingly, HER2+ tumors with high and low expression levels of the stroma signature showed 45% and 85% distant metastasis free survival at 5-year follow-up respectively (HR 2.53; CI 1.31-4.90; p=5.29 10⁻³).

Preliminary results highlight the importance of tumor epithelial-stroma cell interactions in breast carcinogenesis and breast cancer sub-typing. Moreover, it shows the role of stroma cells in tumor dissemination particularly within the HER2+ subtype and provide basis for the development of novel therapeutic strategies.

Investigation of the Tumor Invasion and Immune Response Using in Silico Data Material and Methods Gene Expression Data

Gene expression datasets were retrieved from public databases or authors' website. The inventors have used normalized data (log 2 intensity in single-channel platforms or log 2 ratio in dual-channel platforms) as published by the original studies. No processing of gene expression data was necessary because of the meta-analytical framework of this study.

Probe Annotation and Mapping

Hybridization probes were mapped to Entrez GeneID [19] through sequence alignment against RefSeq mRNA in the (NM) subset, similar to the approach by Shi et al.[20], using RefSeq version 21 (2007.01.21) and Entrez database version 2007.01.21. When multiple probes were mapped to the same GeneID, the one with the highest variance in a particular dataset was selected to represent the GeneID.

Prototype-Based Co-Expression Modules

The inventors have considered a set of prototypes, i.e. genes known to be related to specific biological processes in breast cancer (BC) and aimed to identify the genes that are specifically co-expressed with each of them. To this end, the inventors computed for each gene the direct and the combined associations. The direct association is defined as the linear correlation between gene i and each prototype j separately, whereas the combined association is defined as the linear correlation between gene i and the best linear combination of prototypes, as identified by feature selection (orthogonal Gram-Schmidt feature selection [21]). Considering all the direct and combined associations obtained for gene i, a Friedman's test was used in order to identify the significantly highest associations. In case only one direct association (with prototype j) was left over, then gene i was assigned to module j and was noted as “specific” to prototype j. In contrast, if the highest associations included the multivariate association or several direct associations, then gene i was not assigned to any module j and was noted as “related” to all prototypes involved in the highest associations. A threshold on correlation allowed us to discard the genes that were not correlated to any prototypes. This method was applied in a meta-analytical framework, combining results from NKI2 (4) and VDX (16) datasets (581 patients, see Table 1). Table 1 represents characteristics of the publicly available gene expression datasets. Note that some samples are used in several studies. The following study ids have samples in common: NKI/NKI2 and UPP/STK/UNT/TBAGD/TBVDX/TAM. For all analyses, the inventors removed duplicated patients from small datasets (e.g. NKI) to avoid decreasing the sample size of large datasets (e.g. NKI2).

TABLE 1 Number of patients Gene Data- (% of untreated expression set Id patients) platform NKI NKI 117 (95.8%) Agilent NKI NKI2 295 (55.9%) Agilent Stanford STNO2 STNO2 122 (18%) Microarray cDNA National NCI NCI 99 (11.1%) Cancer Institute MGH MGH 60 (0%) Arcturus UPP UPP 251 (68.1%) Affymetrix STK STK 159 (unknown) Affymetrix VDX VDX 286 (100%) Affymetrix VDX2 VDX2 180 (100%) Affymetrix UNT UNT 137 (100%) Affymetrix UNC UNC 153 (0%) Affymetrix TRANSBIG TBAGD 307 (100%) Affymetrix TRANSBIG TBVDX 198 (100%) Affymetrix TAM TAM 255 (0%) Affymetrix

The whole procedure is sketched in Supplementary FIG. 1. In order to identify genes that are coexpressed with one specific prototype, the inventors used a database of 581 patients from NKI2 and VDX datasets. First, they considered only the intersection of genes between the Affymetrix and Agilent platforms after having applied the mapping procedure as described above (see Section Probe annotation and mapping). The inventors refer hereafter to NKI2 and VDX reduced datasets as gene expressions of this intersection. The following procedure, sketched in Supplementary FIG. 1, is performed for each gene of the NKI2 and VDX reduced datasets:

1 All univariate linear models were fitted using prototypes as explanatory variable and the gene i as response variable in the NKI2 and VDX reduced datasets, resulting in seven couples of univariate linear models. 2 To test whether variability in coefficient estimates between the two platforms are due to sampling error alone, the inventors applied a stringent test of heterogeneity [Cochrane, 1954; 25] for each couple of coefficients. If at least one coefficients is heterogeneous (p-value<0.01), gene i was discarded for further analysis. 3 The inventors compared a set of linear models to identify if gene i is predictable by only one prototype, i.e. one model is significantly better than all the other candidates. To do so, we used the PRESS statistic [Allen, 1974; ref 22] to compute efficiently the leave-one-out cross-validation (LOOCV) errors and compared two models on the basis of their vector of LOOCV errors. A Friedman's test was used to identify the set of best models for NKI2 and VDX reduced datasets separately. For each comparison, the two p-values were meta-analytically combined using the Z-transform method [Whitlock, 2005]. A model was considered as significantly better than another one if the combined p-value<0.05. Because of computational limitation, we were not able to test all possible combinations of prototypes to predict gene i. Only the best set of prototypes with respect to mean squared LOOCV error of the corresponding multivariate linear model was identified using the orthogonal Gram-Schmidt feature selection [Chen et al., 1989]; ref 21. This multivariate model was used in addition to the set of univariate models. 4 The inventors tested the specificity of gene to one prototype by looking at this set of best models. If only one univariate model belonged to this set, it meant that the model using only the prototype j was significantly better than all the models with the other prototypes. Additionally, if the multivariate model belonged to the set of best models, it meant that the multivariate model is not significantly better than the model with prototype j. 5 Gene i was identified to be specific to prototype j and was included in the module, also called gene list, j. In order to reduce the size of the modules, we filtered the specific genes using a threshold of 0.95 on the normalized mean squared LOOCV error.

Module Scores

For a specific dataset, the module score was computed for each sample as:

${{Module}\mspace{14mu} {score}} = {\sum\limits_{i}{{WiXi}{\sum\limits_{i}{{Wj}}}}}$

where x_(i) is the expression of a gene in the module that is present in the dataset's platform. w_(i) is either +1 or −1 depending on the sign of the association with the prototypes. Robust scaling was performed on each module score to have the interquartile range equals to 1 and the median equals to 0 within each dataset, allowing for comparison between module scores.

Gene Ontology and Functional Analysis

Gene ontology analyses were executed using Ingenuity Pathways Analysis tools (Ingenuity Systems, Mountain View, Calif. www.ingenuity.com), a web-delivered application that enables the discovery, visualization, and exploration of molecular interaction networks in gene expression data. The lists of genes identified to be specifically associated with the different prototypes, containing the HUGO gene symbol as well as an indication of positive or negative co-expression, were uploaded into the Ingenuity pathway analysis and correlated with the functional annotations stored in the Ingenuity pathway knowledge base.

Clustering

In order to consistently identify molecular subgroups across the different datasets, we clustered the tumors using the ER (ESR1) and HER2(ERBB2) module scores by fitting Gaussian mixture models [23] with equal and diagonal variance for all clusters. The inventors have used the Bayesian Information Criterion [24] to test the number of components. Each tumor was automatically classified to one of the identified molecular subgroups using the maximum posterior probability of membership in the clusters.

Association Analysis

The inventors have estimated the pairwise correlation of the module scores using Pearson's correlation coefficient. Each correlation coefficient was estimated for each dataset separately and combined with inverse variance-weighted method with fixed effect model [25]. Additionally, the inventors have tested the association between module scores and subtypes using Kruskal-Wallis test. The inventors have tested the association between module scores and clinical variables using Wilcoxon rank sum test. Each statistical test was applied for each dataset separately and p-values were combined using the inverse normal method with fixed effect model [29]. These association analyses were carried out both in the global population and in the different molecular subgroups.

Survival Analysis

The inventors have considered the relapse-free survival (RFS) of untreated patients as the survival endpoint. When RFS was not available, the inventors have used distant metastasis free survival (DMFS) data. All the survival data were censored at 10 years. Survival curves were based on Kaplan-Meier estimates, with the Greenwood method for computing the 95% confidence intervals. Hazard ratios between two or three groups (subtypes and ternary module scores) were calculated using Cox regression with the dataset as stratum indicator, thus allowing for different baseline hazard functions between cohorts. For clinical variables and module scores, the hazard ratios were estimated for each dataset separately and combined with inverse variance-weighted method with fixed effect model [25]. The inventors have used a forward stepwise feature selection in a meta-analytical framework to identify the best multivariable Cox models. The significance thresholds regarding the combined p-values (Wald test for hazard ratio) for the inclusion of a new feature (variable) and for the exclusion of a previously selected feature (variable) were set to 0.05.

Application of the Prognostic Gene Signatures

When cross-platform mapping was necessary, the inventors have only considered genes in the signatures that could be mapped to GeneID. A prediction score was computed for each signature, using a linear combination similar to the formula for module score above. Gene-specific weights (coefficients, correlations, or other measures) from the original studies were converted in +1 or −1 depending on the original up- or down-regulation of each gene. This computation method for previously published gene classifiers gave very similar results compared to the official classifications on the original datasets and allowed the application of gene signatures on different micro-array platforms. Robust scaling was performed on each gene signature to have the interquartile range equals to 1 and the median equals to 0 within each dataset, to allow for comparison between the different gene signatures.

Results

FIGURE LEGEND

FIG. 1 represents joint distribution between the ER (ESR1) and HER2(ERBB2) module scores for three example datasets: NKI2 (A), UNC (B), VDX (C). Clusters are identified by Gaussian mixture models with three components. The ellipses shown are the multivariate analogs of the standard deviations of the Gaussian of each cluster.

FIG. 2 represents survival curves for untreated patients stratified by molecular subtypes ESR1−/ERBB2−, ERBB2+ and ESR1+/ERBB2−.

FIG. 3 represents forest plots showing the log 2 hazard ratios (and 95% CI) of the univariate survival analyses in the global population (A) and in the ESR1−/ERBB2− (B), the ERBB2+ (C) and in the ESR1+/ERBB2− (D) subgroups of untreated breast cancer patients.

FIG. 4 represents Kaplan-Meier curves of the module scores which were significant in the univariate analysis in the molecular subgroup analysis. The module scores were split according to their 33% and 66% quantiles. STAT1 module in the ESR1−/ERBB2− subgroup (A), PLAU module in the ERBB2+ subgroup (B), STAT1 module in the ERBB2+ module (C), AURKA module in the ESR1+/ERBB2− subgroup (D).

FIG. 5 shows the Kaplan-meier survival curves for the ERB2+ subgroup of patients having low, intermediate and high scores for the combination of the tumor invasion and immune module scores.

FIG. 6 sketches the method used to identify prototype-based co-expression modules.

DEFINING THE MOLECULAR MODULES OF BREAST CANCER

To develop the molecular modules the inventors have first selected typical genes to act as “prototypes” for each biological process, based on the literature and then applied a comparison of linear models (see methods) to generate modules of genes specifically associated with each of the prototype genes underlying different biological processes in breast cancer. The selected prototype genes were: AURKA (also known as STK6, 7 or 15), PLAU (also known as uPA), STAT1, VEGF, CASP3, ER (ESR1) and HER2(ERBB2), representing the proliferation, tumor invasion/metastasis, immune response, angiogenesis, apoptosis phenotypes and the ER (ESR1) and HER2 signaling respectively.

To identify genes that would perform well across multiple micro-array platforms and different breast cancer populations, the inventors have defined these molecular modules by analyzing a database of 581 breast tumors samples included in the van de Vijver et al. [4], and Wang et al. series [16], hybridized on Agilent and Affymetrix arrays respectively. Each module score was defined by the difference of the sums of the positively and negatively correlated genes for the chosen prototype only. In case a gene was correlated with more than one prototype, then it was not included in any module. These lists of genes are available as Table 2, see below. The inventors then mapped and computed each of these module scores on several published micro-array datasets totaling over 2100-tumor samples (see Table 1).

The main characteristics of these molecular modules are that they are identified as genes that are co-expressed consistently with the chosen prototypes in datasets using Agilent and Affymetrix micro-array platforms and that they are identified without looking at clinical variables and gene annotation.

Characterization of the Genes Included in the Molecular Modules

The seven lists of genes representing the molecular modules, along with their sign, were uploaded into the Ingenuity pathway knowledge database (IPKB) for analysis of functional annotations.

The ER (ESR1) module was composed of 469 genes and as expected characterized by the co-expression of several luminal and basal genes already reported by previous micro-array studies such as XBP1, TFF1, TFF3, MYB, GATA3, PGR and several keratins. Information was found in the IPKB for 326 of these genes and 139 were significantly associated with a particular function such as small molecule biochemistry, cancer-related functions, lipid metabolism, cellular movement, cellular growth and proliferation or cell death. The HER2(ERBB2) module included 28 genes, with nearly half of them co-located on the 17q11-22 amplicon, such as THRA, ITGA3 and PNMT. Sixteen could be used for functional analysis and 15 were significantly associated with the following ontology classes: cancer-related functions, cell-to-cell signaling, cellular growth and proliferation, molecular transport and cell morphology. The proliferation module (AURKA) included 229 genes, with 34 of them represented in the previously reported genomic grade index. One hundred forty-three genes matched the IPKB, out of which 93 were significantly associated with a particular function. As expected, the majority of these genes, such as CCNB1, CCNB2, BIRC5, were involved in cellular growth and proliferation, cancer and cell cycle related functions. The tumor invasion/metastasis module (PLAU) included 68 genes with several metalloproteinases among them. Out of the 55 that mapped the IPKB, 46 were significantly associated with functions such as cellular movement, tissue development, cellular development and cancer-related functions. The immune response module (STAT1) included 95 genes and the functional analysis carried out on 82 of them revealed that the majority was associated with immune response, followed by cellular growth and proliferation, cell-signaling and cell death. The angiogenesis module (VEGF) included 10 genes related with cancer, gene expression, lipid metabolism and small molecule biochemistry and finally the apoptosis module (CASP3) included 9 genes mainly associated with protein synthesis and degradation, as well as cellular assembly and movement.

It is worth noting that for all the prototypes the lists of genes related to each prototype were much longer than the ones presented here, which represent the genes specifically associated to a given prototype taking into account the correlation with the other prototypes (Table 3).

TABLE 3 Nr of genes associated Nr of genes specifically associated Prototype with the prototype* with the prototype** ESR1 990 468 (47%) ERBB2 158 27 (17%) AURKA 730 228 (31%) PLAU 241 67 (28%) STAT1 480 94 (20%) VEGF 307 13 (4%) CASP3 76 9 (12%) Table 3 represents number of genes associated with each prototype. *These numbers represent the number of genes related with a given prototype, i.e. these genes may also be associated with another prototype. **These numbers represent the number of genes specifically associated with a given prototype, which means that these genes are only associated to this prototype and not to others. For example, the expression of chemokine IL8, which has been reported to have pro-angiogenic effects, was indeed associated with the expression of VEGF. However, since its expression was also correlated with the expression of PLAU, it was not included in any module. The apoptosis-related genes BCL2A1, BIRC3, CD2 and CD69 were not integrated in the apoptosis module, as their expression was also associated with ER (ESR1). Also, additional metalloproteases were found to be associated with PLAU, such as MMP1 and MMP9, but as their expression levels were also correlated with ER (ESR1) and STAT1, they were not included in the invasion module. This shows that the different biological processes are most probably interconnected, but here the inventors wanted to make them “specific” in order to better depict their individual impact on breast cancer biology and prognosis (prognostic).

The expression values of the genes included in the different modules were summarized in module scores for further analysis (see the “module score” section in the methods for details regarding the computation).

Identification and Characterization of the ESR1−/ERBB2−, ESR1+/ERBB2− and ERBB2+Molecular Subgroups

Since the inventors wanted to perform the analyses on the global population but also in the different subgroups based on the ER (ESR1) and HER2 modules, they needed to define these three molecular subgroups. To this end, the inventors used a clustering approach which consistently identified the three groups of patients in the different datasets, except for the MGH and VDX2/TBAGD datasets, due to the lack of ESR1− patients and the small number of probes respectively. The clusters for the NKI2, VDX and UNC cohorts are shown in FIG. 1 as an example.

The clinico-pathological characteristics per molecular subgroup are illustrated in Table 4.

TABLE 4 ESR1−/ERBB2− ERBB2+ ESR1+/ERBB2− Number of subgroup subgroup subgroup patients (%) (N = 189) (N = 129) (N = 628) Age ≦50 years 132 (70) 76 (59) 334 (53) >50 years 57 (30) 53 (41) 294 (47) Size ≦2 cm 121 (64) 84 (65) 457 (73) >2 cm 68 (36) 41 (32) 170 (27) Unknown 0 4 (3) 1 (0) Nodal status Negative 166 (88) 109 (84) 578 (92) Positive 23 (12) 15 (12) 45 (7) Unknown 0 5 (4) 5 (1) Tumor grade I 5 (3) 3 (2) 131 (21) II 19 (10) 31 (24) 238 (38) III 151 (80) 70 (54) 189 (30) Unknown 14 (7) 25 (20) 70 (11) Estrogen receptors Negative 161 (85) 67 (52) 35 (5) Positive 27 (14) 58 (45) 588 (94) Unknown 1 (1) 4 (3) 5 (1) Table 4 represents clinico-pathological characteristics per molecular subgroup for the untreated breast cancer patients considered for the survival analyses. As one would expect, the vast majority of the tumors in the ESR1−/ERBB2− and ERSR1+/ERBB2− subgroups were negative and positive respectively for the ER (ESR1) protein status. On the contrary, the ERBB2+ subgroup was composed by a mixture of tumors with regard to the ER (ESR1) protein status. When comparing the survival curves of these three molecular subgroups across all the untreated patients of this meta-analysis, the inventors observed differences between the molecular subgroups, as already reported by others [27-31]. Indeed, the survival curve from the ESR1+/ERBB2− was significantly different from the two others (p=0.03 for ESR1−/ERBB2− and p=0.003 for ERBB2+). However, no difference in survival was noticed between the ESR1−/ERBB2− and ERBB2+ subgroups (p=0.56; see FIG. 2).

Association Between Clinico-Pathological Parameters and Molecular Module Scores

Looking at the information on the 2180 patients, we started by investigating whether there was any association between the different module scores. One interesting finding was for example the positive and negative correlation between the proliferation module score on one hand and the angiogenesis and tumor invasion module scores on the other hand. These associations were conserved throughout the different molecular subtypes, with the highest correlations being observed in the ESR1−/ERBB2− subgroup. All results are provided in Table 5.

TABLE 5 ERBB2 AURKA PLAU VEGF STAT1 CASP3 (A) Global population CASP3 STAT1 0.170 VEGF −0.290 −0.108 PLAU 0.009 −0.007 −0.134 AURKA −0.300 0.421 0.215 0.101 ERBB2 0.001 0.025 0.080 −0.029 −0.000 ESR1 0.170 −0.314 −0.182 −0.108 −0.304 −0.008 (B) ESR1−/ERBB2− subgroup CASP3 STAT1 0.200 VEGF −0.410 −0.265 PLAU 0.009 −0.134 −0.158 AURKA −0.521 0.551 0.035 0.050 ERBB2 −0.032 0.124 0.141 −0.210 −0.220 ESR1 0.293 −0.510 0.051 −0.298 −0.022 −0.037 (C) ERBB2+ subgroup CASP3 STAT1 0.070 VEGF −0.304 −0.402 PLAU 0.140 0.017 −0.255 AURKA −0.201 0.400 0.144 −0.050 ERBB2 0.179 −0.145 0.105 −0.150 −0.012 ESR1 0.165 0.075 −0.214 0.005 −0.287 0.006 (D) ESR1+/ERBB2− subgroup CASP3 STAT1 0.174 VEGF −0.341 −0.213 PLAU −0.006 0.072 −0.144 AURKA −0.360 0.245 0.170 0.112 ERBB2 0.271 −0.087 0.171 −0.045 −0.103 ESR1 0.050 0.171 −0.306 0.262 −0.318 −0.161 Table 5 refers to the following four tables: meta-estimators of pair-wise Pearson's correlation coefficients between module scores of 2180 treated and untreated breast cancer patients from the global population (A), 319 patients from the ESR1−/ERBB2subgroup (B), 252 patients from the ERBB2+ subgroup (C) and 1610 patients from the ESR1+/ERBB2− subgroup (D).

The inventors further sought to characterize the association between the module scores and the well established clinico-pathological parameters such age, tumor size, nodal status, histological grade and ER (ESR1) status defined either by immunohistochemistry (IHC) or by ligand binding assay. Meaningful associations were found, establishing the validity of module scores. For instance, highly significant associations were observed between ESR1/proliferation module scores and ER (ESR1) protein status/histological grade. The inventors also noticed less known or new associations, such as for example a positive association between histological grade and the angiogenesis, immune response and apoptosis module values. The same associations were also reported for nodal involvement. However, the inventors did not observe any association between the invasion module values and the clinico-pathological markers. When investigating these associations in the different molecular subgroups, the inventors found similar associations in the ESR1+/ERBB2− subgroup, with one major difference being the highly significant correlation between the ERRBB2 module scores and the histological grade which was not observed in the global population. On the contrary, very few significant associations were reported in the two other subgroups. These results are summarized in Table 6.

TABLE 6 Age Tumor Size Nodal ESR1-status (≦50 vs (≦2 vs Status (IHC or LAB*, Grade >60 years) >2 cm) (− vs +) − vs +) (1 vs 3) (A) Global population ESR1 + + + − − + + + − − − ERBB2 NS + + + + + NS AURKA NS + + + + + + − − − + + + PLAU NS NS NS NS NS VEGF NS + + + + + − − − + + + STAT1 − NS + + + − − − + + + CASP3 NS + + + + − − + + + (B) ESR1−/ERBB2− subgroup ESR1 NS NS NS NS NS ERBB2 NS NS NS NS NS AURKA NS + + NS − + PLAU NS NS NS NS NS VEGF NS NS NS NS NS STAT1 NS NS NS NS NS CASP3 NS NS NS −− NS (C) ERBB2+ subgroup ESR1 NS NS − + + + − ERBB2 NS NS NS NS NS AURKA NS + + NS NS NS PLAU NS NS NS NS NS VEGF + + NS NS NS NS STAT1 NS NS NS NS NS CASP3 NS NS NS NS NS (D) ESR1+/ERBB2− subgroup ESR1 + + + NS NS + + + − − − ERBB2 NS + + + + NS + + + AURKA NS + + + + + + − − − + + + PLAU NS NS − NS NS VEGF NS + + + + + + + STAT1 − NS + + + − − + + + CASP3 NS + + + + − + + +

Table 6 refers to the following four tables: association between the module scores and the clinico-pathological parameters for the global population (A), ESR1−/ERBB2(B), ERBB2+ (C) and ESR1+/ERBB2− (D) subgroups. The “+” sign represents a positive association between the variables with a p-value comprised between 0.01 and 0.05 (+), between 0.01 and 0.001 (++) and <0.001 (+++). The “−” sign represents a negative association between the variables with a p-value comprised between 0.01 and 0.05 (−), between 0.01 and 0.001 (−−)

Molecular Modules, Clinico-Pathological Parameters and Prognosis (Prognostic)

To evaluate the prognostic value of these module scores in relation with the natural history of the disease the inventors considered only untreated breast cancer patients including 1235 tumor samples. For that purpose the inventors performed both, univariate and multivariate analysis for relapse free survival on systemically untreated patients with a mean follow-up of 7.4 years including well established clinico-pathological variables as well as the molecular modules defined in this study. These analyses were stratified according to the molecular subgroups to take into consideration the differences in survival over time of these three subgroups of patients (see FIG. 2).

In a univariate model, almost all “well-established” clinico-pathological parameters, namely tumor size, histological grade, and nodal invasion, were significantly associated with clinical outcome. Among the molecular modules, proliferation, angiogenesis and immune response also displayed a statistically significant association with relapse free survival. Given the small percentage (6.7%, 83 out of 1225) of patients with nodal involvement, survival analysis results for nodal status should be interpreted with caution. The results of this univariate analysis are illustrated in FIG. 3 and shown in more details in Table 7.

TABLE 7 (A) Global population hr lower.95 upper.95 p n age 0.813 0.630 1.050 1.13 10⁻⁰¹ 876 size 1.641 1.248 2.157 3.90 10⁻⁰⁴ 887 node 2.038 1.249 3.328 4.40 10⁻⁰³ 315 er 0.844 0.581 1.228 3.75 10⁻⁰¹ 888 grade 3.029 1.989 4.611 2.38 10⁻⁰⁷ 802 ESR1 0.801 0.601 1.068 1.31 10⁻⁰¹ 907 ERBB2 1.203 0.984 1.469 7.08 10⁻⁰² 907 AURKA 2.040 1.666 2.497 4.84 10⁻¹² 907 PLAU 1.095 0.939 1.277 2.47 10⁻⁰¹ 907 VEGF 1.346 1.177 1.540 1.52 10⁻⁰⁵ 907 STAT1 0.845 0.715 0.998 4.78 10⁻⁰² 907 CASP3 1.117 0.973 1.281 1.15 10⁻⁰¹ 907 (B) ESR1−/ERBB2− subgroup hazard ratio lower.95 upper.95 p-value n age 0.918 0.485 1.737 7.92 10⁻⁰¹ 133 size 1.388 0.687 2.804 3.61 10⁻⁰¹ 82 node 0.549 0.149 2.020 3.67 10⁻⁰¹ 37 cr 1.348 0.610 2.981 4.60 10⁻⁰¹ 144 grade 0.903 0.212 3.851 8.90 10⁻⁰¹ 89 ESR1 0.938 0.411 2.138 8.78 10⁻⁰¹ 165 ERBB2 1.212 0.757 1.940 4.22 10⁻⁰¹ 161 AURKA 0.721 0.458 1.135 1.57 10⁻⁰¹ 169 PLAU 1.237 0.879 1.739 2.22 10⁻⁰¹ 156 VEGF 1.001 0.737 1.360 9.93 10⁻⁰¹ 165 STAT1 0.698 0.496 0.982 3.92 10⁻⁰² 169 CASP3 1.082 0.771 1.519 6.47 10⁻⁰¹ 165 (C) ERBB2+ subgroup hazard ratio lower.95 upppr.95 p-value n age 1.709 0.862 3.387 1.25 10⁻⁰¹ 108 size 1.171 0.594 2.307 6.48 10⁻⁰¹ 76 node 4.318 1.314 14.192 1.60 10⁻⁰² 29 er 0.795 0.436 1.450 4.54 10⁻⁰¹ 107 grade 0.851 0.285 2.542 7.72 10⁻⁰¹ 95 ESR1 0.880 0.478 1.621 6.82 10⁻⁰¹ 126 ERBB2 0.963 0.650 1.427 8.50 10⁻⁰¹ 126 AURKA 0.796 0.413 1.536 4.97 10⁻⁰¹ 126 PLAU 1.914 1.214 3.018 5.22 10⁻⁰³ 126 VEGF 1.483 1.003 2.195 4.86 10⁻⁰² 126 STAT1 0.595 0.403 0.878 8.99 10⁻⁰³ 126 CASP3 0.993 0.650 1.516 9.73 10⁻⁰¹ 126 (D) ESR1+/ERBB2− subgroup hazard ratio lower.95 upper.95 p-value n age 0.717 0.522 0.985 4.01 10⁻⁰² 598 size 1.813 1.301 2.527 4.45 10⁻⁰⁴ 605 node 233 er 0.658 0.340 1.273 2.14 10⁻⁰¹ 515 grade 3.862 2.418 6.168 1.55 10⁻⁰⁸ 538 ESR1 0.751 0.525 1.073 1.15 10⁻⁰¹ 605 ERBB2 1.348 1.027 1.770 3.13 10⁻⁰² 605 AURKA 2.784 2.219 3.493 9.03 10⁻¹⁹ 598 PLAU 0.963 0.801 1.159 6.91 10⁻⁰¹ 605 VEGF 1.418 1.210 1.661 1.52 10⁻⁰⁵ 605 STAT1 1.031 0.830 1.280 7.85 10⁻⁰¹ 605 CASP3 1.153 0.982 1.354 8.12 10⁻⁰² 605 Table 7 corresponds to univariate analysis of different gene classifiers per molecular subgroup of untreated breast cancer patients. All signatures are considered here as continuous variables. GENE70=70 gene signature [10,4]; GENE76=76 gene signature [16,17]; P53=p53 signature [8]; WOUND=Wound response signature [12,18]; GGI=Genomic Grade Index [9]; ONCOTYPE=21-gene Recurrence Score [14]; IGS: 186-gene “invasiveness” gene signature [13].

In the multivariate analysis (n=775), proliferation [HR=2.48 (1.88-3.28), p=2 10⁻¹⁰], tumor invasion [1.41 (1.16-1.72), p=7 10⁻⁴], immune response [HR=0.72 (0.59-0.87), p=6 10⁻⁴], apoptosis [HR=1.18 (1.00-1.38), p=0.05], histological grade [HR=1.80 (1.12-2.88), p=0.02] were significantly associated with relapse free survival (RFS), with the proliferation module showing the largest HR and the most significant p-value among the molecular modules.

When the inventors considered the prototype genes alone, the performances were less pronounced compared to their respective modules, suggesting that averaging co-expressed genes into a module score is more stable and less dependent to cross-platform comparisons than the expression level of a singe gene.

Molecular Module Scores, Clinico-Pathological Parameters and Prognosis (Prognostic) in the ESR1−/ERBB2−, ESR1+/ERBB2− and ERBB2+Molecular Subgroups

When investigating the prognostic value of the modules and clinico-pathological parameters according to the molecular subgroups defined above, the inventors observed that in the high risk ESR1−/ERBB2− subpopulation (n=169) only the immune response module showed a significant association with clinical outcome in both, univariate and multivariate analyses [HR=0.70 (0.50-0.98), p=0.04] (FIGS. 3-4).

Of interest, proliferation module lost its significance as almost all ER (ESR1) negative tumors showed high proliferation module scores.

In the ESR1+/ERBB2− subpopulation (n=531), age, tumor size and histological grade were associated with RFS, together with the HER2 (ERBB2), proliferation and angiogenesis modules. In multivariate analysis, only the proliferation module [HR=2.68 (2.02-3.55), p=9 10⁻¹²] and histological grade [HR=2.00 (1.18-3.37), p=0.01) remained significant, with the proliferation module having the highest HR and the most significant p-value.

In the ERBB2+ tumors (n=126), nodal status, tumor invasion, angiogenesis and immune response modules or scores were significantly associated with RFS in the univariate model whereas only tumor invasion [HR=2.07 (1.32-3.25), p=0.001] and immune response [HR=0.56 (0.36-0.86), p=0.009] modules remained significantly associated with RFS in the multivariate model. The inventors then sought to combine these two variables in order to improve classification. Weights of +1 and −1 were used in the combination of the tumor invasion and immune response modules respectively. However, this simple combination did not significantly improve the classification of patients in the ERBB2+ subgroup with respect to prognosis (prognostic) as shown in FIG. 5.

Dissecting Prognostic Gene Expression Signatures Using Molecular Modules

In order to investigate the biological meaning of the individual genes included in several published prognostic signatures (10, 4, 16, 17, 12, 18, 9, 14, 8, 13), the inventors applied the same comparison of linear models to several prognostic signatures in order to define which molecular category each individual gene included in these signatures belongs to. Table 8 illustrates the percentage of genes of each signature related to or specifically associated (value in brackets) with a particular prototype.

TABLE 8 AURKA PLAU VEGF STAT1 CASP3 ESR1 ERBB2 (Proliferation) (Invasion) (Angiogenesis) (Immune response) (Apoptosis) GENE70 73% 60% 63% 47% 43% 29% 60% (10%)  (0%) (14%)  (3%)  (0%)  (1%)  (0%) GENE76 38% 35% 55% 42% 26% 30% 16%  (3%)  (0%) (16%)  (5%)  (1%)  (0%)  (1%) P53 88% 53% 53% 47% 28% 19% 38% (34%)  (0%) (16%)  (0%)  (0%)  (3%)  (0%) WOUND 42% 30% 52% 39% 35% 30% 40%  (4%)  (0%) (13%)  (3%)  (1%)  (0%)  (3%) GGI 73% 37% 99% 64% 43% 43% 30%  (1%)  (2%) (54%)  (0%)  (0%)  (0%)  (0%) ONCOTYPE 69% 44% 69% 38% 25% 25% 38% (19%)  (6%) (13%)  (6%)  (0%)  (0%)  (0%) IGS 34% 20% 40% 40% 31% 22% 19% (10%)  (0%) (10%)  (4%)  (1%)  (2%)  (0%)

This analysis demonstrated that more than half of the genes in each signature investigated in this study were statistically associated with the proliferation prototype. Also the highest percentages of specific association, i.e. association with one prototype but not with the others, were also reported for AURKA, highlighting the importance of proliferation in several prognostic signatures.

The inventors further found that CD10 and/or PLAU signatures according to Tables 11 and/or 13 correlate with resistance to chemotherapy (anthracyclin).

The inventors use CD10 and/or PLAU signatures as diagnosis and/or to assist the choice of suitable medicine.

The inventors then went a step further by comparing the prognostic value of each molecular module of the “dissected” signature with the original one for three of the above reported prognostic gene signatures: the 70 gene [10,4], the 76 gene [16,17] and the genomic grade [9]. To do so, the inventors have used the TRANSBIG independent validation series of untreated primary breast cancer patients on which these signatures were computed using the original algorithms and micro-array platforms [5, 26], providing also the advantage that this population was not used for the development of any of these signatures. The inventors compared the hazard ratios for distant metastasis free survival for the group of genes from the original signatures, which were specifically associated with one of the prototypes, with the hazard ratio obtained with the original ones. Interestingly, as shown in FIG. 8, the performances of the proliferation modules were equivalent to the original signatures for all three investigated signatures, suggesting that proliferation might be the driving force. FIG. 8 represents forest plots showing the log 2 hazard ratios (and 95% CI) of the univariate analyses carried out on the TRANSBIG validation data [18-19] using the dissected signatures of GENE70=70 gene signature [1-2] (A), GENE76=76 gene signature [3-4] (B) and GGI=Genomic Grade Index [7] (C).

Evaluating the Impact of the Prognostic Signatures in the Different Molecular Subgroups

In order to investigate which molecular subtype of breast cancer may benefit from these prognostic signatures the inventors analyzed the prognostic impact of the different gene signatures reported above in the different molecular subgroups defined by the ER (ESR1) and HER2 (ERBB2) molecular module scores. Since the exact algorithms for generating the different gene signatures cannot be applied on different micro-array platforms, the inventors decided to compute the classifiers as done for the module scores, using the direction of the association reported in the respective initial publications. Being concerned by the fact that a signed average might be less efficient than the original algorithm, the inventors conducted some comparison studies on original publications and found that the original and modified scores were highly correlated and that their performances were very similar. Since most predictors are often best described using unimodal distributions and since using dichotomized outcome variables may introduce a significant bias in comparing different prognostic signatures, the inventors considered here the different signatures as continuous variables. Also, it should be noted that given the application of robust scaling, the different signatures can be compared to one another.

The analysis of the prognostic power of these signatures by molecular subgroup, which was carried out only on patients which were not used in the development of these predictors, showed that the performance of these signatures seemed to be confined to the ESR1+/ERBB2-subgroup of patients (Table 9). Indeed the different signatures were not informative at all in the two other molecular subgroups.

TABLE 9 ESR1−/ERBB2− ERBB2+ ESR1+/ERBB2− HR Nr of HR Nr of HR Nr of (95% CI) p-value patients (95% CI) p-value patients (95% CI) p-value patients GENE70 1.12 0.60 154 1.29 0.36 120 2.11 3 10⁻¹⁰ 566 (0.73-1.72) (0.75-2.20) (1.67-2.66) GENE76 1.30 0.32 99 0.81 0.42 85 1.52 2 10⁻⁵  422 (0.78-2.15) (0.49-1.34) (1.24-1.88) P53 1.01 0.98 163 1.04 0.92 126 2.23 4 10⁻⁷  605 (0.42-2.42) (0.51-2.11) (1.64-3.03) WOUND 0.90 0.54 160 1.24 0.35 126 1.48 5 10⁻⁶  598 (0.65-1.26) (0.79-1.93) (1.25-1.75) GGI 0.78 0.38 165 0.79 0.48 126 3.16 2 10⁻¹⁹ 598 (0.44-1.36) (0.40-1.53) (2.46-4.06) ONCOTYPE 0.86 0.74 156 1.00 1.00 126 4.79 3 10⁻²⁰ 605 (0.36-2.08) (0.50-2.02) (3.43-6.68) IGS 1.08 0.70 169 0.96 0.85 126 2.12 6 10⁻¹³ 605 (0.73-1.61) (0.63-1.46) (1.73-2.60)

In this study, the inventors developed molecular modules representing several biological processes previously described in breast cancer, i.e. proliferation, tumor invasion, immune response, angiogenesis, apoptosis, as well as estrogen and HER2 (ERBB2) signalling. Although by dissecting breast cancer into its molecular components we simplified the nature of the disease, this study yielded a wealth of information regarding the understanding of the main biological processes involved in breast cancer and their impact on prognosis (prognostic).

The inventors first identified seven lists of genes representing the molecular modules. The module comprising the highest number of genes was the ER (ESR1) module (468 genes). This was not surprising since several publications on the molecular classification of breast cancer have repeatedly and consistently identified the estrogen receptor status of breast cancer as the main discriminator of expression subgroups [27, 28, 29, 30]. The second list with the highest number of genes was the one related to proliferation module (228 genes), which is consistent with the findings reported previously by Sotiriou et al. [30]. In contrast to these long lists, the modules reflecting angiogenesis, apoptosis and HER2 (ERBB2) signalling only ended up with a very limited number of genes, 13, 9 and 27 genes respectively. This can be partially explained by the fact that many genes associated with these modules were also associated with ER (ESR1) or proliferation (AURKA) and therefore not retained in the development of the other molecular modules.

The functional analysis of this molecular modules revealed also interesting information. As expected, many genes included in these modules were known to be associated with the chosen biological process. But many others, representing sometimes more than half of the module, were not yet reported to be related with breast cancer or were previously reported to be associated with another biological phenotype.

Investigating the relationship between traditional clinico-pathological markers and the different molecular modules revealed a positive association between the ER (ESR1) module and the age of the patient, an association which has been reported frequently for the protein levels of ER (ESR1) [31], as well as with the ER (ESR1) status, underlining a very good correlation between protein and expression levels of ER (ESR1).

Interestingly, the inventors observed a positive association between the HER2 (ERBB2) module and the ER (ESR1) protein expression status. As it has been suggested that the clinical efficacy of endocrine therapy might be compromised by the presence of HER2 (ERBB2) amplification or over-expression [32, 33, 34, 35, 36], the interrelationship of ER (ESR1) and HER2 (ERBB2) has come to have an important role in the management of breast cancer. Although the amplification/over-expression of HER2 (ERBB2) is generally inversely correlated with the expression of ER (ESR1), the precise extend of this correlation has only recently been reported by Lal et al. [37] in a large series of 3,655 breast cancer tumors using two of the standardized FDA-approved methods for HER2 (ERBB2) testing. Interestingly, they reported that almost half of the HER2 (ERBB2) positive tumors (49.1%) still expressed ER (ESR1). This supports the present finding that HER2 (ERBB2) module-positive tumors are associated with a positive ER (ESR1) protein status.

The inventors did not observe any association between the tumor invasion module (PLAU) and the clinico-pathological markers. This is in agreement with the study published by Leissner et al. [38], who investigated the mRNA expression of PLAU in lymph-node and hormone-receptor positive breast cancer.

Regarding the angiogenesis module, Bolat et al. also observed a positive correlation between VEGF and tumor size, although interestingly this finding seemed to be restricted to invasive ductal and not lobular carcinomas [39].

In a study involving 73 breast cancer patients, Widchwendter et al. found that high STAT1 activation was a significant predictor of good prognosis (prognostic) independent of the well-known prognosis (prognostic) markers and that the only parameter that correlated with STAT1 activation was the nodal status, the majority of tumors derived from LN-negative patients being associated with a high STAT1 activation [40], which is what the inventors also reported. This observation is in agreement with the fact that node-negative patients and high STAT1 are associated with a better prognosis (prognostic).

Breast cancer is a clinically heterogeneous disease. Several groups have consistently identified different molecular subclasses of breast cancer, with the basal-like (mostly ER (ESR1) and HER2 (ERBB2) negative) and HER2 (ERBB2) (mostly HER2 (ERBB2) amplified) subgroups showing the shortest relapse-free and overall survival, whereas the luminal-like type (estrogen receptor-positive) tumors had a more favorable clinical outcome (summarized in [41]). As we can no longer ignore the fact that these subgroups represent different types of breast cancer disease, we conducted the same analysis in the three subgroups identified by the main discriminators: ER (ESR1) and HER2 (ERBB2).

In the ESR1+/ERBB2− subgroup, proliferation module and histological grade were the two variables which remained associated with survival in the multivariate analysis, with the proliferation module having the most significant p-value. This is consistent with the finding that two clinically distinct ESR1-positive molecular subgroups can be defined by the genomic grade [6]. In the ERBB2+ subgroup, tumor invasion and immune response appeared to be the main processes associated with tumor progression. This finding supports that mRNA expression of PLAU was a powerful prognostic indicator in HER2 (ERBB2) positive tumors [42].

In the third subgroup (ESR1−/ERBB2−), only immune response appeared to predict prognosis (prognostic). It has been reported that tumors which do not express the hormone receptors and HER2 (ERBB2), commonly called the “triple-negative” or ‘basal-like” tumors, are more aggressive. Given their triple negative status, these patients cannot be treated with the conventional targeted therapies currently available for breast cancer, such as endocrine or HER2 (ERBB2)-targeted therapies, leaving chemotherapy as the only weapon.

In this context, several authors have suggested that chemotherapy might be more efficient in this subtype of the disease [43, 44]. However defining the optimal chemotherapy regimen remains controversial. Since BRCA1 pathway activity seems to be impaired in many of these tumors and since BRCA1 functions in DNA repair and cell cycle checkpoints, some authors have suggested that these tumors might be associated with sensitivity to DNA-damaging chemotherapy and may also be associated with resistance to spindle poisons [49]. In this study, the inventors showed that impaired immune response might be linked with the development of distant metastases (in this particular subgroup of patients). Indeed, high expression levels of the immune module (Tables 10 and 11) were associated with a significantly better outcome, both at the univariate and multivariate level.

It has been shown that STAT1 is particularly important in activating interferon-γ (IFN-γ) and its antitumor effects. In addition to inhibiting proliferation and survival, IFN-γ enhances the immunogenicity of tumor cells in part through enhancing STAT1-dependent expression of MHC proteins [46]. Based on this observation and the fact that an attenuated STAT1 signalling in tumors might be correlated with their malignant behavior, Lynch et al. recently postulated that enhancing gene transcription mediated by STAT1 may be an effective approach to cancer therapy [47]. Therefore, they screened 5,120 compounds and identified one molecule, 2-(1,8-naphthyridin-2-yl)phenol, that enhanced gene activation mediated by STAT1 over that seen with maximally efficacious concentration of IFN. Since STAT1 activation seems to be an important element in the killing of tumor cells in response to cytotoxic agents through repression of pro-survival genes and activation of apoptosis genes, its activation may be particularly important in patients receiving chemotherapy and particularly in these ESR1−/ERBB2− patients where most therapeutic approaches rely on cytotoxic agents that induce cell death in a nonspecific manner.

When the inventors dissected the main prognostic gene signatures reported so far in the literature to better understand their biological meaning, the inventors noticed that they were all composed by a significant proportion of proliferation-related genes. Also when the inventors compared the original signatures with their molecular modules in an independent series of patients, they noticed that the proliferation genes contained in the original signature were able to resume its prognostic performance. This underlines the fact that proliferation-related genes appear to be a common denominator of several existing prognostic gene expression signatures. Since defects in cell cycle deregulation are a fundamental characteristic of breast cancer, it is not surprising that these genes are involved in breast cancer prognosis (prognostic). Several studies showed indeed that increased expression of cell-cycle and proliferation-associated genes was correlated with poor outcome (reviewed in [48]). There are of course differences in the exact proliferation-associated genes, due to the difference in population analyzed or platform used. Although the use of proliferation-associated cell markers is not new, for example the protein expression levels of Ki67 and PCNA have already been used as prognostic markers for decades, gene expression profiling studies suggested that measuring proliferation using a more objective, automated and quantitative assay may be more robust compared to the less quantitative assays such as immunohistochemistry.

By investigating the prognostic ability of the main gene signatures reported so far according to the different breast cancer subtypes, the inventors have observed that the prognostic power of these signatures was limited to the ESR1+/ERBB2− molecular subgroup composed by estrogen receptor-positive patients. This is in agreement with the findings that: 1) proliferation seems to be the main contributor of these signatures and 2) the ESR1+/ERBB2− subgroup is the only molecular subgroup displaying a wide range of proliferation values.

This finding also emphasizes the need of additional prognostic markers for the other two molecular subgroups, and more specifically for the ESR1−/ERBB2− subgroup, which is associated with a poor prognosis (prognostic) and limited therapeutic options. Therefore, the inventors believe that by studying the immune response mechanisms in this particular subgroup of patients might help to better understand these tumors and to develop efficient targeted therapies.

To conclude, by identifying molecular modules representing the main biological mechanisms involved in breast cancer, the inventors were able to better characterize the biological foundation of the different prognostic signatures and to understand the mechanisms that trigger the different tumors to progress. These findings may help to define new clinico-genomic models and to identify new targets in the specific molecular subgroups, in order to make a step towards truly personalized medicine.

Investigation of the Immune Response by Studying CD4+ Cells

The inventors have profiled CD4+ cells isolated from primary invasive ductal carcinomas. An unsupervised, hierarchical clustering algorithm allowed the inventors to distinguish two groups of tumors which were different regarding the pathways involved in immune response. Considering these immune pathways, 118 genes that are differentially expressed in tumor infiltrating CD4+ cells were identified and they generated a gene signature called “CD4 infiltrating tumor signature” (CD4ITS) that differs substantially from previously reported gene signatures in breast cancer. The relationship between CD4ITS and clinical outcome in more than 2600 patients listed in public datasets was also analysed. An important finding was that the CD4ITS was associated with the risk of metastasis in patients with subtype 1 breast carcinoma who are usually associated with the worst prognosis (prognostic).

Materials and Methods

Patient's samples. Patients with invasive ductal breast carcinoma were recruited for the study. No patient had received any adjuvant systemic therapy. Human breast carcinoma tissues were obtained at the time of the surgery.

Patient datasets. Nine gene expression datasets obtained by micro-array analysis of tumor specimens from a total of 2641 patients with primary breast cancer were used: the dataset from van de Vijver 2002⁴, Buyse 2006⁵, Desmedt 2007²⁶, Loi 2007⁶, Sotiriou 2003⁷, Miller 2005⁸, Sotiriou 2006⁹, van't veer 2002¹⁰ and Sorlie 2003¹¹.

Isolation of CD4+ cells. A procedure to isolate CD4+ cells from ductal breast carcinoma was established. Briefly, carcinoma samples were mechanically dissociated using a scalpel. Fragments were incubated in 12-well culture dish with a mixture of Collagenase-Type 4 (Worthington) in x-vivo media (BioWhittaker) in a 37° C. incubator with 5% CO₂ with constant agitation for 20-60 min, depending of the size of the sample. Following dissociation, the digestion product were filtered through a nylon mesh using piston syringe and washed with x-vivo. The CD4+ cells were isolated form the unicellular suspension using Dynal® CD4 Positive Isolation Kit according to the manufacturer's instructions. The purity of the population was checked by flow cytometry.

Flow cytometry. To verify the quality of the T CD4+ cells isolation, the inventors have analyzed CD3, CD4 and CD8 surface expression by flow cytometry were analyzed. For this issue, beads of an aliquot of cells were detached according to the manufacturer's procedure. Briefly, 5 μl of each specific OItest conjugated antibody (Beckman Coulter) was added to the test tube containing cells resuspended in 50 μl HAFA buffer (RPMI 1640 without phenol red (BioWhittaker), 3% inactivated FBS, 20 mM NaN₃). The tube was vortexed and incubated for 30 minutes at 4° C., protected from the light. Cells were washed with PBS and fixed in 2% paraformaldehyde. Fluorescence analysis was performed by use of a FACSCalibur (BD Biosciences).

Isolation of RNA from lymphocytes. The RNA was extracted from fresh CD4+ cells using the phenol/chloroform procedure with TriPure Isolation Reagent (Roche Applied Science). Briefly, Tripure (1 ml) was added to each tube containing CD4+ cells. The tubes were vortexed and chloroform was added. Samples were placed on a Phase Lock Gel™ (Expenders) and centrifuged at 15682 rcf. The upper aqueous phase was removed and placed in a new tube. Isopropanol and glycogen were added, and then the tube was centrifuged to precipitate the RNA. The RNA pellet was washed twice with 75% ethanol, dried using Speedvack, and resuspended in nuclease-free water. The amount and the quality of RNA were respectively determined using the Nanodrop and the Agilent Capiler System.

Gene expression analysis. 10 patient's breast carcinomas with a sufficient amount of good quality RNA were isolated from purified CD4+ cells infiltrating primary tumour. Micro-array analysis was performed with Affymetrix U133Plus Genechips (Affymetrix). RNA two-cycle amplification, hybridation and scanning were done according to standard Affymetrix protocols. Image analysis and probe quantification was performed with the Affymetrix software that produced raw probe intensity data in the Affymetrix CEL files. The program RMA was use to normalise the data.

Statistical analysis. Considering the 10 expression profiles of CD4+ cells isolated from invasive ductal carcinomas, an unsupervised, hierarchical clustering was established. On the basis of the BioCarta pathways, the difference between the clusters was analysed. Genes involved in pathways related to the immune response and presenting a significant difference in the expression level were selected to compose the CD4ITS. A score, called the CD4ITS index (CD4ITSI) was introduced to summarize the similarity between the expression profile related to the immune reaction and the clinical outcome. Considering genes composing the CD4ITS, the CD4ITSI was defined as the sum of the fold change in upregulated genes subtracted from the sum of the fold change in downregulated genes. This score was then calculated for each patient listed in the datasets (n=2641). The datasets were exploited in whole or distinguishing the different subtypes of patient's tumors and/or the (un)administration of any therapy. Univariate and multivariate analyses of relapse with the use of the Cox proportional-hazards method were performed with the use of SPSS, version 15.0. To estimate the rates of overall metastasis-free survival along the time, the Kaplan-Meier method was used. In this issue, considered patient's data were then sorted by ascending score and a cutoff point was defined at 75^(th) percentile which divided the patients into two groups. Patients with low and high scores were assigned respectively to the group 1 and 2. Results were illustrated on survival curves.

Results—Expression Profile of Tumor Infiltrating CD4+ Cells Differs According to the Er Status.

Using the micro-array technology, the genetic profiles of CD4+ cells isolated from 10 breast carcinomas was established namely 5 ER+ and 5 ER−. Regarding these profiles, an unsupervised clustering revealed 2 main clusters. Interestingly, these two clusters correspond practically to the ER status of the tumor. These clusters were very stable and reproducible using different clustering methods (centered, uncentered, completed or average linkage).

Localisation CD4+—Th1/Th2—Generation of the CD4+ infiltrating tumor signature (CD4ITS).

Considering the cellular pathways, the difference between the two main clusters which divide the expression profiles of the CD4+ cells infiltrating mammary tumors was examined. There were 37 statistically significant pathways which differed between the two clusters. Interestingly, 31 of those pathways were associated with immune reaction. A genetic signature, called the “CD4+ infiltrating tumor signature” (CD4ITS) was established. To access this issue, genes involved in these 31 immune pathways on the basis of a significant difference (p value<0.05) were selected.

The CD4ITS and outcome in breast cancer. The CD4ITS index (CD4ITSI) was calculated for each patient in the databases using the formula described in the patients and methods section. This index was tested for its association with clinical outcomes in a time relapse-free survival analysis using Cox proportional-hazards model in several datasets (n=2641). Considering this whole dataset, a low correlation was revealed between the CD4ITSI and the clinical outcome, with hazard ratios of 0.909 (95% CI, 0.840 to 0.984; P=0.018). Considering this result three subtypes of breast carcinomas, namely Esr1− Erbb2− (subtype 1 or “basal-like”), Erbb2+ (subtype2) and Esr1+ Erbb2− (subtype3 or “luminal”), were distinguish for discerning samples on the basis of these subtypes. Results showed a strong and statistically significant correlation between CD4ISI and the clinical outcome on subtype 1 breast carcinoma, with hazard ratios of 0.733 (95% CI, 0.620 to 0.867; P=0.000). A similar correlation was shown regarding the subtype 2 but with a slighter effect, with hazard ratios of 0.790 (95% CI, 0.635 to 0.982; P=0.033). No correlation was displayed with subtype 3, with hazard ratios of 0.920 (95% CI, 0.812 to 1,042; P=0.187).

To make further investigation among patient with subtype 1 breast carcinoma and to estimate the time relapse-free survival, the Kaplan-Meier method was used. In this issue, the patients were stratified according to the CD4ITS as described in the patients and methods section. The estimated 5-years rates of overall metastasis-free survival were 57.7% (CD4ITSI<75^(th) percentile) and 81.8% (CD4ITSI≧75^(th) D percentile).

The prognostic value of the CD4IS on treated and untreated patients with subtype 1 breast cancer was investigated. The prognostic value of CD4ITS is stronger on treated patients, with hazard ratios of 0.673 (95% CI, 0.512 to 0.884; P=0.004), than on untreated patients, with hazard ratios of 0.792 (95% CI, 0.638 to 0.983; P=0.034) (see table 4). The Kaplan-Meier method was performed as described above, the estimated 5-years rates of overall metastasis-free survival among treated and untreated patients were 48.7% (CD4ITSI<75^(th) percentile) and 81.5% (CD4ISI≧75^(th) percentile); 60.9% (CD4ITSI<75^(th) percentile) and 81.25% (CD4ISI≧75^(th) percentile) respectively.

The CD4ITS and other prognostic signatures. To estimate the robustness of the signature, according to the invention, the inventors have compared CD4ITS to the published predictive signatures, namely Wound¹², IGS¹³, Oncotype¹⁴, GGI⁹, Gene 70⁴, Gene 76¹⁵, on the treated and/or untreated patients with subtype 1 breast cancer. A Cox proportional-hazards model showed that CD4ITS was the unique signature which had a statistically significant predictive value among patient with subtype 1 breast cancer with hazard ratio of 0.733 (95% CI, 0.620 to 0.867; P=0.000). Discerning treated and untreated patients, the exclusive validity of the CD4ITS is strongly conserved among the treated one.

TABLE 2 module EntrezGene.ID HUGO.gene.symbol agilent affy coefficient NMSE ESR1 2099 ESR1 NM_000125 205225_at 1 0 23158 TBC1D9 AB020689 212956_at 0.818853934 0.329519058 2625 GATA3 NM_002051 209602_s_at 0.808404454 0.340901046 771 CA12 NM_001218 204508_s_at 0.769664466 0.403723308 3169 FOXA1 NM_004496 204667_at 0.747740313 0.445912639 4602 MYB NM_005375 204798_at 0.724360247 0.476220193 7802 DNALI1 NM_003462 205186_at 0.722064641 0.476993136 18 ABAT NM_020686 209459_s_at 0.68431164 0.500878387 7494 XBP1 NM_005080 200670_at 0.706606341 0.504567097 57758 SCUBE2 NM_020974 219197_s_at 0.706307294 0.507028611 2066 ERBB4 AF007153 214053_at 0.705524131 0.50920309 9 NAT1 NM_000662 214440_at 0.68994857 0.524568765 10551 AGR2 NM_006408 209173_at 0.682493984 0.524896233 987 LRBA M83822 212692_s_at 0.667204458 0.545200585 56521 DNAJC12 AF176012 218976_at 0.654147619 0.552279601 2203 FBP1 NM_000507 209696_at 0.666017848 0.563765784 51466 EVL NM_016337 217838_s_at 0.653404963 0.564019798 51442 VGLL1 NM_016267 215729_s_at −0.66129561 0.567442475 57496 MKL2 NM_014048 218259_at 0.64903192 0.567499146 7031 TFF1 NM_003225 205009_at 0.6449711 0.567670532 1153 CIRBP NM_001280 200810_s_at 0.644376986 0.57712969 26227 PHGDH NM_006623 201397_at −0.64928809 0.582061385 1555 CYP2B6 M29873 206754_s_at 0.631227682 0.596212258 6648 SOD2 NM_000636 215223_s_at −0.62622708 0.605433039 55638 NA NM_017786 218692_at 0.629800859 0.605503031 221061 C10orf38 AL050367 212771_at −0.61911622 0.620120942 7033 TFF3 NM_003226 204623_at 0.616219874 0.620667764 53335 BCL11A NM_018014 219497_s_at −0.61751635 0.624593924 79818 ZNF552 Contig43054 219741_x_at 0.610820144 0.627481194 57613 KIAA1467 AB040900 213234_at 0.590842681 0.631251573 8416 ANXA9 NM_003568 210085_s_at 0.600083497 0.632229077 582 BBS1 Contig1503_RC 218471_s_at 0.607975339 0.634990977 54463 NA NM_019000 218532_s_at 0.601669708 0.636624769 55733 HHAT NM_018194 219687_at 0.57829406 0.638592631 2674 GFRA1 NM_005264 205696_s_at 0.584823646 0.638780117 4478 MSN NM_002444 200600_at −0.59183487 0.643848416 51097 SCCPDH NM_016002 201825_s_at 0.594863448 0.646197689 54502 NA NM_019027 218035_s_at 0.597290216 0.649932337 26018 LRIG1 AL117666 211596_s_at 0.591723382 0.65103686 55793 FAM63A NM_018379 221856_s_at 0.586608892 0.655692588 3868 KRT16 NM_005557 209800_at −0.54949798 0.660555073 54961 SSH3 NM_017857 219919_s_at 0.580160177 0.662407239 60481 ELOVL5 AF111849 208788_at 0.582552358 0.663927448 3667 IRS1 NM_005544 204686_at 0.57148821 0.670004986 83439 TCF7L1 Contig57725_RC 221016_s_at −0.57685166 0.670185709 10950 BTG3 NM_006806 205548_s_at −0.57803585 0.671668378 3572 IL6ST NM_002184 204863_s_at 0.566168955 0.672265327 4783 NFIL3 NM_005384 203574_at −0.55143972 0.674600099 51161 C3orf18 NM_016210 219114_at 0.553100882 0.675614902 2296 FOXC1 NM_001453 213260_at −0.56246613 0.677073594 6664 SOX11 NM_003108 204914_s_at −0.57838974 0.677177874 5613 PRKX NM_005044 204061_at −0.55539077 0.679650809 8543 LMO4 NM_006769 209204_at −0.56711672 0.680574997 55686 MREG NM_018000 219648_at 0.57186844 0.680694279 8100 IFT88 NM_006531 204703_at 0.55028445 0.682287138 2617 GARS NM_002047 208693_s_at −0.56419322 0.684354279 3945 LDHB NM_002300 201030_x_at −0.55557485 0.685360876 8382 NME5 NM_003551 206197_at 0.555210673 0.689486281 10614 HEXIM1 NM_006460 202815_s_at 0.5516074 0.690267345 9633 MTL5 NM_004923 219786_at 0.561763365 0.692112214 2568 GABRP NM_014211 205044_at −0.55883521 0.693312003 23324 MAN2B2 AB023152 214703_s_at 0.555058606 0.693977059 55765 C1orf106 NM_018265 219010_at −0.54180004 0.695474669 5104 SERPINA5 J02639 209443_at 0.552615794 0.696714554 5174 PDZK1 NM_002614 205380_at 0.546051055 0.697188944 56674 TMEM9B Contig1462_RC 218065_s_at 0.528127412 0.698235582 1054 CEBPG NM_001806 204203_at −0.55314581 0.698369112 9120 SLC16A6 NM_004694 207038_at 0.548877174 0.701189497 79641 ROGDI Contig292_RC 218394_at 0.54629249 0.701533185 23303 KIF13B AF279865 202962_at 0.541898896 0.702905771 2173 FABP7 NM_001446 205029_s_at −0.52941225 0.703037328 23171 GPD1L D42047 212510_at 0.544914666 0.705950088 9674 KIAA0040 NM_014656 203143_s_at 0.532088271 0.708978452 27134 TJP3 NM_014428 213412_at 0.542775525 0.710067869 79921 TCEAL4 Contig3659_RC 202371_at 0.541970152 0.710331465 54898 ELOVL2 AL080199 213712_at 0.52925655 0.710508034 1345 COX6C NM_004374 201754_at 0.539941313 0.710572245 5937 RBMS1 NM_016839 207266_x_at −0.53974436 0.711344043 400451 NA AL110139 51158_at 0.537420183 0.716062616 3898 LAD1 NM_005558 203287_at −0.53550815 0.716693669 2530 FUT8 NM_004480 203988_s_at 0.505530007 0.718532442 51306 C5orf5 NM_016603 218518_at 0.528812601 0.719378071 25837 RAB26 NM_014353 219562_at 0.526164961 0.719523191 10982 MAPRE2 X94232 202501_at −0.51938230 0.721044346 1632 DCI NM_001919 209759_s_at 0.5213171 0.721375708 7905 REEP5 M73547 208873_s_at 0.525130991 0.725825747 1101 CHAD NM_001267 206869_at 0.526770704 0.726408365 323 APBB2 U62325 213419_at 0.507242904 0.729583221 28958 CCDC56 NM_014019 218026_at 0.523641457 0.729997843 1476 CSTB NM_000100 201201_at −0.52228528 0.730310348 9435 CHST2 NM_004267 203921_at −0.52396710 0.730941092 7371 UCK2 NM_012474 209825_s_at −0.51709149 0.733658287 2737 GLI3 NM_000168 205201_at 0.521494671 0.733707267 8685 MARCO NM_006770 205819_at −0.51838499 0.73371596 3295 HSD17B4 NM_000414 201413_at 0.49793269 0.738043938 11013 TMSL8 D82345 205347_s_at −0.48243814 0.738461069 51604 PIGT NM_015937 217770_at 0.514231244 0.738548025 6663 SOX10 NM_006941 209842_at −0.52250076 0.739074324 85377 MICALL1 Contig55538_RC 221779_at −0.51653462 0.739527411 58495 OVOL2 AL079276 211778_s_at 0.509854248 0.740100478 1116 CHI3L1 NM_001276 209395_at −0.50752539 0.741531574 11001 SLC27A2 NM_003645 205768_s_at 0.504487267 0.743254132 25841 ABTB2 AL050374 213497_at −0.50152319 0.744291557 64080 RBKS Contig54394_RC 57540_at 0.501098938 0.744631881 375035 SFT2D2 AL035297 214838_at −0.48888167 0.745192165 10479 SLC9A6 NM_006359 203909_at −0.46218527 0.746780768 5002 SLC22A18 NM_002555 204981_at 0.498450997 0.747634385 8645 KCNK5 NM_003740 219615_s_at −0.50676541 0.748157343 79885 HDAC11 AL137362 219847_at 0.503640516 0.748262024 11254 SLC6A14 NM_007231 219795_at −0.46793656 0.748739207 122616 C14orf79 AF038188 213512_at 0.508580125 0.749420609 79650 C16orf57 Contig56298_RC 218060_s_at −0.51270039 0.749551419 23321 TRIM2 AB011089 202341_s_at −0.50510712 0.749962222 23327 NEDD4L AB007899 212448_at 0.502371307 0.750281297 22977 AKR7A3 NM_012067 206469_x_at 0.49969396 0.750370918 8581 LY6D X82693 206276_at −0.49652701 0.750473705 8842 PROM1 NM_006017 204304_s_at −0.49873779 0.750894641 4953 ODC1 NM_002539 200790_at −0.50017862 0.752229895 55544 RBM38 X75315 212430_at −0.48523095 0.752354883 55663 ZNF446 NM_017908 219900_s_at 0.502643541 0.752376668 27124 PIB5PA U45975 213651_at 0.493911581 0.753414597 6715 SRD5A1 NM_001047 211056_s_at −0.49787464 0.756655029 51809 GALNT7 NM_017423 218313_s_at 0.491503578 0.757011056 89927 C16orf45 Contig1239_RC 212736_at 0.491495819 0.757310477 1827 DSCR1 NM_004414 208370_s_at −0.45318343 0.757687519 51706 CYB5R1 NM_016243 202263_at 0.480014471 0.75876488 3383 ICAM1 NM_000201 202638_s_at −0.4921546 0.759111299 5806 PTX3 NM_002852 206157_at −0.50095406 0.759263083 9501 RPH3AL NM_006987 221614_s_at 0.489345723 0.759692293 3613 IMPA2 NM_014214 203126_at −0.49271114 0.759753232 7568 ZNF20 AL080125 213916_at 0.474191523 0.760393024 6280 S100A9 NM_002965 203535_at −0.48574767 0.761593701 22929 SEPHS1 NM_012247 208941_s_at −0.49031224 0.762710604 81563 C1orf21 Contig56307 221272_s_at 0.48956231 0.762763451 1389 CREBL2 NM_001310 201990_s_at 0.468866383 0.764274897 1410 CRYAB NM_001885 209283_at −0.49071498 0.764626005 10884 MRPS30 NM_016640 218398_at 0.479596064 0.765432562 55614 C20orf23 AK000142 219570_at 0.486726442 0.765836231 1824 DSC2 Contig49790_RC 204750_s_at −0.48878224 0.765994757 7851 MALL U17077 209373_at −0.48905517 0.766316309 2743 GLRB NM_000824 205280_at 0.480525648 0.766572036 427 ASAH1 NM_004315 210980_s_at 0.474147175 0.766857518 5241 PGR NM_000926 208305_at 0.507968301 0.767931467 51364 ZMYND10 NM_015896 205714_s_at 0.465885335 0.768320131 6926 TBX3 NM_016569 219682_s_at 0.467758204 0.768972653 5193 PEX12 NM_000286 205094_at 0.465534987 0.771299562 8531 CSDA NM_003651 201161_s_at −0.48379436 0.771700739 23 ABCF1 AF027302 200045_at −0.45941767 0.771727802 7545 ZIC1 NM_003412 206373_at −0.47973354 0.77245107 819 CAMLG NM_001745 203538_at 0.470697705 0.772933304 2947 GSTM3 NM_000849 202554_s_at 0.477492539 0.773863567 5825 ABCD3 NM_002858 202850_at 0.478558366 0.774199051 5860 QDPR NM_000320 209123_at 0.466880459 0.77694304 59342 SCPEP1 Contig51742_RC 218217_at −0.46539062 0.777429767 51806 CALML5 NM_017422 220414_at −0.43692661 0.777841349 79603 LASS4 Contig55127_RC 218922_s_at 0.44467496 0.780061636 21 ABCA3 NM_001089 204343_at 0.476768516 0.780354714 54847 SIDT1 NM_017699 219734_at 0.457175309 0.78051878 8537 BCAS1 NM_003657 204378_at 0.471260926 0.781068878 10874 NMU NM_006681 206023_at −0.40879552 0.782327854 54149 C21orf91 NM_017447 220941_s_at −0.45741133 0.782940362 9929 JOSD1 NM_014876 201751_at −0.45878624 0.785508213 5317 PKP1 NM_000299 221854_at −0.47574048 0.785750041 7388 UQCRH NM_006004 202233_s_at −0.46334012 0.786324045 64764 CREB3L2 AL080209 212345_s_at −0.44888154 0.78771472 10127 ZNF263 NM_005741 203707_at 0.459983171 0.78860236 80347 COASY U18919 201913_s_at 0.441985485 0.788930057 126353 C19orf21 Contig53480_RC 212925_at 0.448608295 0.789172076 50865 HEBP1 NM_015987 218450_at 0.446561227 0.790515478 54812 AFTPH Contig44143 217939_s_at 0.455170453 0.791035737 64087 MCCC2 AL079298 209624_s_at 0.462857334 0.792137211 8884 SLC5A6 AL096737 204087_s_at −0.43982908 0.793363126 5269 SERPINB6 S69272 211474_s_at 0.46113414 0.793737295 4321 MMP12 NM_002426 204580_at −0.44026565 0.793907251 8190 MIA NM_006533 206560_s_at −0.42956164 0.794003971 6769 STAC NM_003149 205743_at −0.46154415 0.794035744 51368 TEX264 NM_015926 218548_x_at 0.435409448 0.794574725 23541 SEC14L2 NM_012429 204541_at 0.449863872 0.795691113 9185 REPS2 NM_004726 205645_at 0.442965761 0.796203486 185 AGTR1 NM_000685 205357_s_at 0.448719626 0.796491882 7368 UGT8 NM_003360 208358_s_at −0.47320635 0.797181557 399665 FAM102A AL049365 212400_at 0.426089803 0.797887209 12 SERPINA3 NM_001085 202376_at 0.430128647 0.798346485 55975 KLHL7 NM_018846 220238_s_at −0.44715312 0.799331759 25864 ABHD14A AL050015 210006_at 0.431227602 0.799391044 4851 NOTCH1 NM_017617 218902_at −0.44628024 0.800453543 9091 PIGQ NM_004204 204144_s_at 0.448022351 0.800799077 1299 COL9A3 NM_001853 204724_s_at −0.43453156 0.801359118 2800 GOLGA1 NM_002077 203384_s_at 0.432417726 0.801979288 8326 FZD9 NM_003508 207639_at −0.46571299 0.802324839 6376 CX3CL1 NM_002996 203687_at −0.44647627 0.802408813 8399 PLA2G10 NM_003561 207222_at 0.441846629 0.802595278 5327 PLAT NM_000931 201860_s_at 0.446276147 0.802779242 22885 ABLIM3 NM_014945 205730_s_at 0.446223817 0.803580219 11094 C9orf7 NM_017586 219223_at 0.438954737 0.803900187 5321 PLA2G4A M68874 210145_at −0.42416523 0.80390189 57348 TTYH1 NM_020659 219415_at −0.45165274 0.805615356 6787 NEK4 NM_003157 204634_at 0.438354592 0.807293759 123872 LRRC50 AL137334 222068_s_at 0.423132817 0.808146112 10421 CD2BP2 NM_006110 202257_s_at 0.438472091 0.809185652 5971 RELB NM_006509 205205_at −0.42058475 0.810752119 6833 ABCC8 NM_000352 210246_s_at 0.43299799 0.811094072 11122 PTPRT NM_007050 205948_at 0.441958947 0.811634327 23650 TRIM29 NM_012101 211002_s_at −0.41153904 0.812560427 79629 OCEL1 Contig49281_RC 205441_at 0.402331924 0.812866251 8722 CTSF NM_003793 203657_s_at 0.436109995 0.813444547 57110 HRASLS NM_020386 219984_s_at −0.43040468 0.813917579 6697 SPR NM_003124 203458_at 0.374042555 0.815469964 2919 CXCL1 NM_001511 204470_at −0.43103914 0.815720462 27250 PDCD4 AL049932 212593_s_at 0.42229844 0.815720916 23245 ASTN2 AB014534 215407_s_at 0.432272945 0.81655549 10265 IRX5 NM_005853 210239_at 0.444238765 0.816746883 2824 GPM6B Contig448_RC 209170_s_at −0.42759793 0.8168277 10644 IGF2BP2 NM_006548 218847_at −0.40137448 0.817753304 7436 VLDLR NM_003383 209822_s_at −0.41016150 0.81824919 25825 BACE2 NM_012105 217867_x_at −0.42961248 0.818674706 10827 C5orf3 NM_018691 218588_s_at 0.427773891 0.819304526 4828 NMB M21551 205204_at −0.42674501 0.820247788 6720 SREBF1 NM_004176 202308_at 0.417450053 0.820708855 10477 UBE2E3 NM_006357 210024_s_at −0.42413489 0.822164226 3066 HDAC2 NM_001527 201833_at −0.42527142 0.822454328 55224 ETNK2 NM_018208 219268_at 0.400594749 0.823435185 875 CBS NM_000071 212816_s_at −0.36357167 0.823556622 3872 KRT17 NM_000422 205157_s_at −0.39795768 0.82378018 753 C18orf1 NM_004338 207996_s_at 0.423862631 0.823845166 136 ADORA2B NM_000676 205891_at −0.42306361 0.823856862 2013 EMP2 NM_001424 204975_at 0.421077857 0.824624291 1917 EEF1A2 NM_001958 204540_at 0.430874995 0.825239707 3576 IL8 NM_000584 202859_x_at −0.42263800 0.825795247 419 ART3 NM_001179 210147_at −0.43304415 0.825917814 55650 PIGV NM_017837 51146_at 0.420582519 0.826931805 23107 MRPS27 D87453 212145_at 0.406366641 0.826940683 25818 KLK5 NM_012427 222242_s_at −0.41340419 0.827115168 8309 ACOX2 NM_003500 205364_at 0.408316599 0.827876009 1047 CLGN NM_004362 205830_at 0.369392157 0.82901223 10002 NR2E3 NM_014249 208388_at 0.407775212 0.830043531 60487 TRMT11 Contig54010_RC 218877_s_at −0.40566142 0.830431941 10656 KHDRBS3 NM_006558 209781_s_at −0.40340408 0.831344622 55240 STEAP3 NM_018234 218424_s_at −0.41466295 0.83324228 3315 HSPB1 NM_001540 201841_s_at 0.406168651 0.834031319 10273 STUB1 NM_005861 217934_x_at 0.413376875 0.834700244 2171 FABP5 NM_001444 202345_s_at −0.41219044 0.835111923 55184 C20orf12 NM_018152 219951_s_at 0.39674387 0.835120573 5783 PTPN13 NM_006264 204201_s_at 0.392109759 0.835383296 1877 E4F1 NM_004424 218524_at 0.400337951 0.83577919 11098 PRSS23 NM_007173 202458_at 0.408630816 0.836021917 10202 DHRS2 NM_005794 214079_at 0.394698247 0.836221587 80223 RAB11FIP1 Contig1682_RC 219681_s_at 0.409041709 0.836355265 79627 OGFRL1 Contig39960_RC 219582_at −0.41147589 0.836715105 6948 TCN2 NM_000355 204043_at −0.40164819 0.836747162 3097 HIVEP2 NM_006734 212641_at −0.40364447 0.838742793 8985 PLOD3 NM_001084 202185_at −0.40629339 0.83937633 3892 KRT86 X99142 215189_at −0.40898783 0.839394877 10575 CCT4 NM_006430 200877_at −0.40322219 0.839667184 51004 COQ6 NM_015940 218760_at 0.40443291 0.839743802 4071 TM4SF1 M90657 215034_s_at −0.4024996 0.839926234 1718 DHCR24 D13643 200862_at 0.380176977 0.839949625 1381 CRABP1 NM_004378 205350_at −0.40429027 0.8409904 9368 SLC9A3R1 NM_004252 201349_at 0.405852497 0.841380916 92104 TTC30A AL049329 213679_at 0.403451511 0.841551015 9518 GDF15 NM_004864 221577_x_at 0.402707288 0.841948716 6364 CCL20 NM_004591 205476_at −0.36319472 0.842019711 3306 HSPA2 U56725 211538_s_at 0.395674599 0.842245746 79605 PGBD5 Contig53598_RC 219225_at −0.40705584 0.84277541 23336 DMN AB002351 212730_at −0.39034362 0.843586584 1356 CP NM_000096 204846_at −0.40404337 0.843884436 54619 CCNJ NM_019084 219470_x_at −0.38111750 0.844401655 9200 PTPLA NM_014241 219654_at −0.39972249 0.844778941 51302 CYP39A1 NM_016593 220432_s_at −0.33695618 0.844975117 5191 PEX7 NM_000288 205420_at 0.396991099 0.845179405 706 TSPO NM_007311 202096_s_at −0.39169845 0.845341528 7159 TP53BP2 NM_005426 203120_at −0.39572610 0.845767077 55218 EXDL2 NM_018199 218363_at 0.401498328 0.846250153 79669 C3orf52 Contig53814_RC 219474_at 0.388442276 0.846776039 10140 TOB1 NM_005749 202704_at 0.367622466 0.84725245 11226 GALNT6 Contig49342_RC 219956_at 0.395283101 0.847253692 6652 SORD NM_003104 201563_at 0.394652204 0.847767541 3418 IDH2 NM_002168 210046_s_at −0.40013914 0.847804159 10200 MPHOSPH6 NM_005792 203740_at −0.39554753 0.848141674 7345 UCHL1 NM_004181 201387_s_at −0.37679195 0.84953539 6564 SLC15A1 NM_005073 207254_at −0.34318347 0.850903361 54458 PRR13 NM_018457 217794_at 0.392279425 0.850920162 51103 NDUFAF1 NM_016013 204125_at 0.353122452 0.85105789 11042 NA NM_006780 215043_s_at 0.388381527 0.851937806 10040 TOM1L1 NM_005486 204485_s_at 0.382624539 0.852751814 1117 CHI3L2 U49835 213060_s_at −0.37689236 0.853033349 112398 EGLN2 NM_017555 220956_s_at 0.392095205 0.853446237 9258 MFHAS1 NM_004225 213457_at −0.32447140 0.85362056 374 AREG NM_001657 205239_at 0.375610148 0.854146851 2982 GUCY1A3 NM_000856 221942_s_at −0.38254572 0.854163644 688 KLF5 NM_001730 209211_at −0.39113342 0.854558871 1960 EGR3 NM_004430 206115_at 0.373008187 0.85611316 7993 UBXD6 NM_005671 215983_s_at 0.382878926 0.856242287 25823 TPSG1 NM_012467 220339_s_at 0.373878408 0.856591509 4485 MST1 L11924 205614_x_at 0.357450422 0.857946991 23528 ZNF281 NM_012482 218401_s_at 0.379127283 0.858339794 1672 DEFB1 NM_005218 210397_at −0.39076646 0.858685673 28960 DCPS NM_014026 218774_at −0.38267717 0.858774643 5268 SERPINB5 NM_002639 204855_at −0.35802733 0.859249445 934 CD24 NM_013230 209772_s_at −0.36282951 0.86062728 55450 CAMK2N1 NM_018584 218309_at 0.370660238 0.860945792 6261 RYR1 NM_000540 205485_at −0.35082856 0.861340834 2627 GATA6 NM_005257 210002_at −0.37081347 0.862200066 57180 ACTR3B NM_020445 218868_at −0.38659759 0.862506996 4036 LRP2 NM_004525 205710_at 0.350254766 0.86266905 29116 MYLIP NM_013262 220319_s_at 0.373793594 0.862681243 57211 GPR126 AL080079 213094_at −0.37693751 0.862687147 4435 CITED1 NM_004143 207144_s_at 0.375304645 0.862985246 54913 RPP25 NM_017793 219143_s_at −0.37237191 0.86390199 9982 FGFBP1 NM_005130 205014_at −0.33016268 0.864260466 11170 FAM107A NM_007177 209074_s_at −0.35901803 0.864884193 3294 HSD17B2 NM_002153 204818_at −0.38270805 0.866150203 6583 SLC22A4 NM_003059 205896_at 0.323184257 0.866415185 79170 ATAD4 Contig61975 219127_at 0.373271428 0.867669413 79745 CLIP4 Contig48631 219944_at −0.27836229 0.86848439 2813 GP2 NM_016295 214324_at 0.346238895 0.868853586 6723 SRM NM_003132 201516_at −0.34578620 0.870266606 1360 CPB1 NM_001871 205509_at 0.346493776 0.871724386 5016 OVGP1 NM_002557 205432_at 0.340204667 0.872087776 5271 SERPINB8 NM_002640 206034_at −0.35808395 0.872952965 347902 AMIGO2 Contig49079_RC 222108_at 0.36104055 0.87334578 79719 NA Contig57044_RC 202851_at 0.364020628 0.874136088 55258 NA NM_018271 219044_at 0.358273868 0.874179008 8563 THOC5 NM_003678 209418_s_at −0.35724536 0.874354782 83464 APH1B Contig53314_RC 221036_s_at 0.38272656 0.874569471 23532 PRAME NM_006115 204086_at −0.35189188 0.87568013 6834 SURF1 NM_003172 204295_at 0.360498545 0.876816575 6019 RLN2 NM_005059 214519_s_at 0.340131262 0.877580596 214 ALCAM NM_001627 201951_at 0.357195699 0.878486882 55333 SYNJ2BP NM_018373 219156_at 0.354152982 0.878595717 10525 HYOU1 NM_006389 200825_s_at −0.35389917 0.879309158 2232 FDXR NM_004110 207813_s_at 0.357851956 0.88094545 274 BIN1 NM_004305 210202_s_at −0.36200933 0.8810547 10307 APBB3 NM_006051 204650_s_at 0.346101202 0.882638244 8986 RPS6KA4 NM_003942 204632_at −0.33810477 0.882825424 56938 ARNTL2 NM_020183 220658_s_at −0.35442683 0.883130457 9510 ADAMTS1 NM_006988 222162_s_at −0.31714081 0.883576407 2770 GNAI1 NM_002069 209576_at −0.34021112 0.883662467 4350 MPG NM_002434 203686_at 0.341676941 0.884004809 863 CBFA2T3 NM_005187 208056_s_at 0.344392794 0.884416124 2891 GRIA2 NM_000826 205358_at 0.325402619 0.884813944 10309 UNG2 X52486 210021_s_at 0.340406908 0.884921127 7037 TFRC NM_003234 207332_s_at −0.33653368 0.884923454 3574 IL7 NM_000880 206693_at −0.34389077 0.885221043 55293 UEVLD NM_018314 220775_s_at 0.344688842 0.885938381 27165 GLS2 NM_013267 205531_s_at 0.254837341 0.886441129 55188 RIC8B NM_018157 219446_at 0.342486332 0.887434273 11202 KLK8 NM_007196 206125_s_at −0.35998705 0.887541757 51181 DCXR NM_016286 217973_at 0.299804251 0.88771423 827 CAPN6 NM_014289 202965_s_at −0.32896134 0.888075448 390 RND3 Contig3682_RC 212724_at −0.33533047 0.888607585 54438 GFOD1 NM_018988 219821_s_at −0.33775830 0.889053494 10079 ATP9A AB014511 212062_at 0.328282857 0.889255142 4285 MIPEP NM_005932 36830_at 0.356463366 0.889469146 8324 FZD7 NM_003507 203706_s_at −0.33206439 0.889884855 9052 GPRC5A NM_003979 203108_at 0.346433922 0.890040223 9508 ADAMTS3 AB002364 214913_at −0.29195187 0.890309433 10519 CIB1 NM_006384 201953_at 0.318187791 0.890742687 7138 TNNT1 NM_003283 213201_s_at 0.331611482 0.891033522 51735 RAPGEF6 NM_016340 219112_at 0.326267887 0.89116631 54970 TTC12 NM_017868 219587_at 0.291552597 0.891346796 2591 GALNT3 NM_004482 203397_s_at −0.34242172 0.891358691 2348 FOLR1 NM_000802 204437_s_at −0.32727835 0.891730283 2954 GSTZ1 NM_001513 209531_at 0.334740431 0.891823109 23318 ZCCHC11 D83776 212704_at −0.28744690 0.891980859 10267 RAMP1 NM_005855 204916_at 0.331220193 0.892185659 25984 KRT23 NM_015515 218963_s_at −0.33772871 0.89242928 6496 SIX3 NM_005413 206634_at −0.26458260 0.892787299 786 CACNG1 NM_000727 206612_at 0.325288477 0.893132764 22976 PAXIP1 U80735 212825_at 0.314975901 0.893439408 283232 TMEM80 Contig52603_RC 221951_at 0.334733545 0.894635943 629 CFB NM_001710 202357_s_at 0.325947876 0.895246912 7286 TUFT1 NM_020127 205807_s_at 0.324287679 0.8957374 5562 PRKAA1 NM_006251 209799_at −0.27248266 0.897249406 9851 KIAA0753 NM_014804 204711_at 0.33776741 0.897696217 79622 C16orf33 Contig52526_RC 218493_at 0.313083514 0.898920401 55316 RSAD1 NM_018346 218307_at 0.329901495 0.898981065 6271 S100A1 NM_006271 205334_at −0.32519543 0.899120454 55859 BEX1 NM_018476 218332_at 0.315589822 0.899579486 3595 IL12RB2 NM_001559 206999_at −0.34467894 0.900222341 5100 PCDH8 NM_002590 206935_at −0.35519567 0.900356755 2861 GPR37 NM_005302 209631_s_at −0.31562942 0.902920283 26278 SACS NM_014363 213262_at −0.29589301 0.903024533 55506 H2AFY2 NM_018649 218445_at −0.31488076 0.904286521 64215 DNAJC1 Contig3538_RC 218409_s_at 0.309391077 0.904704283 3096 HIVEP1 NM_002114 204512_at −0.30420168 0.905214361 23059 CLUAP1 AB014543 204577_s_at 0.308081913 0.905659063 79602 ADIPOR2 Contig41209_RC 201346_at 0.294636455 0.905943382 56683 C21orf59 NM_017835 218123_at 0.30298336 0.906330205 22943 DKK1 NM_012242 204602_at −0.31707767 0.906552011 6277 S100A6 NM_014624 217728_at −0.31127446 0.906567008 65983 GRAMD3 AL157454 218706_s_at −0.31070593 0.906845373 4255 MGMT NM_002412 204880_at 0.306014355 0.906934039 10406 WFDC2 NM_006103 203892_at 0.310318913 0.908053059 3760 KCNJ3 NM_002239 207142_at 0.289824264 0.90907496 23552 CCRK NM_012119 205271_s_at 0.281880641 0.910569983 9722 NOS1AP AB007933 215153_at 0.229340894 0.911497251 23613 PRKCBP1 AB032951 209049_s_at 0.299807266 0.911563244 202 AIM1 U83115 212543_at −0.28250629 0.912039471 51207 DUSP13 NM_016364 219963_at 0.295957672 0.913470799 83988 NCALD AF052142 211685_s_at −0.27863454 0.913549975 2920 CXCL2 NM_002089 209774_x_at −0.23251798 0.913929307 8870 IER3 NM_003897 201631_s_at 0.293240479 0.914353765 55245 C20orf44 NM_018244 217935_s_at 0.292257279 0.914633438 6666 SOX12 NM_006943 204432_at 0.288976299 0.91494091 80279 CDK5RAP3 AK000260 218740_s_at 0.295086243 0.915477346 1644 DDC NM_000790 205311_at −0.25539982 0.915582189 5441 POLR2L NM_021128 202586_at 0.290705454 0.915792241 9022 CLIC3 NM_004669 219529_at −0.29342331 0.915932573 7769 ZNF226 NM_015919 219603_s_at 0.291518083 0.91618188 27239 GPR162 NM_019858 205056_s_at 0.267327121 0.916259358 26504 CNNM4 NM_020184 218900_at 0.299283579 0.916676204 3400 ID4 NM_001546 209291_at −0.29901729 0.917135234 1733 DIO1 NM_000792 206457_s_at 0.277146054 0.918178806 25915 C3orf60 AL049955 209177_at 0.275728009 0.918466799 1525 CXADR NM_001338 203917_at −0.29399348 0.918866262 1475 CSTA NM_005213 204971_at −0.29629654 0.919065795 2155 F7 NM_019616 207300_s_at 0.291791149 0.919083227 4188 MDFI NM_005586 205375_at −0.29462263 0.919236535 3622 ING2 NM_001564 205981_s_at 0.290622475 0.919303599 25980 C20orf4 NM_015511 218089_at 0.203116625 0.919391746 8310 ACOX3 NM_003501 204242_s_at 0.287582101 0.919961112 54820 NDE1 NM_017668 218414_s_at 0.282080137 0.920079592 5816 PVALB NM_002854 205336_at 0.227358785 0.920203757 60686 C14orf93 Contig51318_RC 219009_at 0.24607044 0.920539974 8792 TNFRSF11A NM_003839 207037_at −0.30152349 0.920541992 54894 RNF43 NM_017763 218704_at 0.280441269 0.923270824 5737 PTGFR NM_000959 207177_at −0.2231448 0.924206492 1501 CTNND2 U96136 209618_at 0.273276047 0.924383316 7764 ZNF217 NM_006526 203739_at 0.276000692 0.925380013 8405 SPOP NM_003563 208927_at 0.270754072 0.926506674 1847 DUSP5 NM_004419 209457_at 0.277032448 0.927166495 4488 MSX2 NM_002449 205555_s_at 0.295463635 0.927546165 7163 TPD52 NM_005079 201691_s_at 0.263461652 0.927805212 25790 CCDC19 NM_012337 220308_at 0.286351098 0.928605166 5803 PTPRZ1 NM_002851 204469_at −0.26445918 0.92970977 23635 SSBP2 NM_012446 203787_at 0.261272248 0.930412837 6548 SLC9A1 S68616 209453_at 0.266541892 0.930417948 8187 ZNF239 NM_005674 206261_at 0.273064581 0.931123654 2588 GALNS NM_000512 206335_at −0.23243233 0.93213956 54903 MKS1 NM_017777 218630_at 0.248040673 0.932362145 55163 PNPO Contig55446_RC 218511_s_at 0.255506984 0.932823779 55101 NA NM_018035 218038_at 0.266549718 0.933387577 4682 NUBP1 NM_002484 203978_at 0.244519893 0.934015928 3779 KCNMB1 NM_004137 209948_at −0.21564509 0.934522794 64849 SLC13A3 AF154121 205243_at −0.27379455 0.935284703 4691 NCL NM_005381 200610_s_at −0.25948109 0.93550478 64428 NARFL Contig41536_RC 218742_at 0.203857245 0.935624333 23266 LPHN2 NM_012302 206953_s_at −0.25295037 0.936162229 29104 N6AMT1 NM_013240 220311_at 0.222484457 0.937942569 1783 DYNC1LI2 NM_006141 203590_at −0.24622451 0.938320864 8987 NA NM_003943 203986_at 0.243504322 0.938630895 79852 ABHD9 Contig21225_RC 220013_at −0.27078394 0.93887984 57586 SYT13 AB037848 221859_at 0.239472393 0.939365745 8785 MATN4 NM_003833 207123_s_at −0.20822884 0.939574568 10331 B3GNT3 NM_014256 204856_at −3 0.940573085 5357 PLS1 NM_002670 205190_at 0.247326218 0.940664991 54880 BCOR Contig26100_RC 219433_at 0.229605443 0.942981745 55790 NA NM_018371 219049_at −0.25042614 0.943118658 4139 MARK1 NM_018650 221047_s_at −0.24475937 0.944329845 81539 SLC38A1 Contig58438_RC 218237_s_at 0.241702504 0.945111586 10810 WASF3 NM_006646 204042_at −0.18215567 0.945444166 926 CD8B NM_004931 215332_s_at −0.24348476 0.945464604 50805 IRX4 NM_016358 220225_at −0.23224835 0.945544554 58513 EPS15L1 NM_021235 221056_x_at 0.233246267 0.94611709 6304 SATB1 NM_002971 203408_s_at −0.23571514 0.946625307 79446 WDR25 Contig50337_RC 219609_at 0.208642099 0.948915101 23366 NA AB020702 213424_at 0.234295176 0.948952138 55699 IARS2 NM_018060 217900_at 0.230870685 0.949477716 ERBB2 2064 ERBB2 NM_004448 216836_s_at 1 0 93210 PERLD1 Contig56503_RC 221811_at 0.907758645 0.17200875 5709 PSMD3 NM_002809 201388_at 0.679856111 0.551760856 5409 PNMT NM_002686 206793_at 0.65236504 0.581082444 55876 GSDML NM_018530 219233_s_at 0.551201489 0.701042445 22794 CASC3 NM_007359 207842_s_at 0.475868476 0.791261269 3927 LASP1 NM_006148 200618_at 0.465455223 0.802630026 147179 WIPF2 U90911 212051_at 0.438708817 0.803363538 55040 EPN3 NM_017957 220318_at 0.402128957 0.840891081 5245 PHB NM_002634 200659_s_at 0.397536834 0.852777893 9635 CLCA2 NM_006536 217528_at 0.36055161 0.867650117 3227 HOXC11 NM_014212 206745_at 0.312754199 0.881082423 29095 ORMDL2 NM_014182 218556_at 0.349298325 0.883214676 5909 RAP1GAP NM_002885 203911_at 0.337350258 0.889359836 1573 CYP2J2 NM_000775 205073_at 0.309379585 0.903278515 26154 ABCA12 AL080207 215465_at 0.292060066 0.908124968 3081 HGD NM_000187 205221_at 0.302330606 0.90880385 8804 CREG1 NM_003851 201200_at −0.29666354 0.915982859 9914 ATP2C2 NM_014861 206043_s_at 0.291958436 0.917143657 5129 PCTK3 AL161977 214797_s_at −0.29470259 0.919581811 54793 KCTD9 NM_017634 218823_s_at −0.28572478 0.919693777 404093 CUEDC1 NM_017949 219468_s_at 0.320633179 0.925765463 3675 ITGA3 NM_002204 201474_s_at 0.274007124 0.927570492 55129 TMEM16K NM_018075 218910_at 0.256032493 0.92892133 24147 FJX1 NM_014344 219522_at −0.25223514 0.939735137 1048 CEACAM5 M29540 201884_at 0.25663632 0.947093755 9572 NR1D1 X72631 204760_s_at 0.244126274 0.94968023 51375 SNX7 NM_015976 205573_s_at −0.23406410 0.949762889 AURKA 6790 AURKA NM_003600 208079_s_at 1 0 11065 UBE2C NM_007019 202954_at 0.820863855 0.332578721 9133 CCNB2 NM_004701 202705_at 0.79214599 0.375663771 1058 CENPA NM_001809 204962_s_at 0.786068713 0.378411034 332 BIRC5 NM_001168 202095_s_at 0.785737371 0.385905904 11004 KIF2C NM_006845 209408_at 0.776738323 0.403529163 10112 KIF20A NM_005733 218755_at 0.7580889 0.420402209 991 CDC20 NM_001255 202870_s_at 0.743241214 0.435115841 2305 FOXM1 U74612 202580_x_at 0.743383899 0.439906192 891 CCNB1 Contig56843_RC 214710_s_at 0.749756817 0.441921351 22974 TPX2 AB024704 210052_s_at 0.748568487 0.468134359 9088 PKMYT1 NM_004203 204267_x_at 0.702883844 0.47437898 54478 FAM64A NM_019013 221591_s_at 0.685128928 0.487318586 4751 NEK2 NM_002497 204641_at 0.718457153 0.487941235 24137 KIF4A NM_012310 218355_at 0.710510621 0.488813369 23397 NCAPH D38553 212949_at 0.72007551 0.490967285 9319 TRIP13 U96131 204033_at 0.710205816 0.499972805 4085 MAD2L1 NM_002358 203362_s_at 0.695603942 0.517656017 9156 EXO1 NM_006027 204603_at 0.673978083 0.540280713 10615 SPAG5 NM_006461 203145_at 0.670442201 0.550833392 7083 TK1 NM_003258 202338_at 0.643196792 0.554895627 6491 STIL NM_003035 205339_at 0.679351067 0.561436112 6241 RRM2 NM_001034 209773_s_at 0.663496582 0.564978476 55839 CENPN NM_018455 219555_s_at 0.665830165 0.566600085 7298 TYMS NM_001071 202589_at 0.65945932 0.568519762 641 BLM NM_000057 205733_at 0.649401343 0.584673125 4171 MCM2 NM_004526 202107_s_at 0.635855115 0.597104864 1164 CKS2 NM_001827 204170_s_at 0.614902417 0.610429408 79682 MLF1IP Contig64688 218883_s_at 0.624317967 0.615339427 10129 FRY U50534 204072_s_at −0.59404899 0.652505205 51659 GINS2 NM_016095 221521_s_at 0.582355702 0.652817049 10212 DDX39 NM_005804 201584_s_at 0.568291258 0.657312844 3925 STMN1 NM_005563 200783_s_at 0.589613162 0.657518464 79801 SHCBP1 Contig34952 219493_at 0.585901802 0.661475953 3014 H2AFX NM_002105 205436_s_at 0.579987829 0.666254194 10535 RNASEH2A NM_006397 203022_at 0.580753923 0.666515392 5984 RFC4 NM_002916 204023_at 0.575746351 0.671194217 55970 GNG12 AL049367 212294_at −0.56373935 0.68491997 1033 CDKN3 NM_005192 209714_s_at 0.575815638 0.6918622 55388 MCM10 NM_018518 220651_s_at 0.572262092 0.69399602 55257 C20orf20 NM_018270 218586_at 0.553371639 0.695442511 1163 CKS1B NM_001826 201897_s_at 0.545468556 0.698030816 8914 TIMELESS NM_003920 203046_s_at 0.559966788 0.704852194 54821 NA NM_017669 219650_at 0.506228567 0.70697648 23371 TENC1 AB028998 212494_at −0.54033843 0.719688949 8544 PIR NM_003662 207469_s_at 0.51732303 0.722573201 8317 CDC7 AF015592 204510_at 0.522596999 0.730034447 2331 FMOD NM_002023 202709_at −0.49793008 0.730688731 51512 GTSE1 NM_016426 215942_s_at 0.522293944 0.737008012 6424 SFRP4 NM_003014 204051_s_at −0.50398156 0.739316208 55353 LAPTM4B NM_018407 208029_s_at 0.510974612 0.741225782 8404 SPARCL1 NM_004684 200795_at −0.50844548 0.744694596 990 CDC6 NM_001254 203967_at 0.503962062 0.748292813 7043 TGFB3 NM_003239 209747_at −0.50101461 0.750780117 11047 ADRM1 NM_007002 201281_at 0.481127919 0.752181185 58190 CTDSP1 NM_021198 217844_at −0.48706893 0.757675543 79838 TMC5 Contig45537_RC 219580_s_at −0.48922140 0.762742558 84823 LMNB2 M94362 216952_s_at 0.492907473 0.765450281 83989 C5orf21 AF070617 212936_at −0.48676706 0.766896872 1793 DOCK1 NM_001380 203187_at −0.48337292 0.768557986 9358 ITGBL1 NM_004791 205422_s_at −0.43649111 0.769646328 8836 GGH NM_003878 203560_at 0.484685676 0.769709668 57088 PLSCR4 NM_020353 218901_at −0.482651 0.770237787 6642 SNX1 AL050148 213364_s_at −0.46500284 0.770486626 4969 OGN NM_014057 218730_s_at −0.46695975 0.770624576 90627 STARD13 AL049801 213103_at −0.48080449 0.770936403 11260 XPOT NM_007235 212160_at 0.472165093 0.772199633 22827 NA AF114818 209899_s_at 0.477068606 0.773496315 9793 CKAP5 D43948 212832_s_at 0.466604145 0.783735263 2791 GNG11 NM_004126 204115_at −0.43671582 0.785914493 55247 NEIL3 NM_018248 219502_at 0.387791125 0.785965193 10234 LRRC17 NM_005824 205381_at −0.47039399 0.78807293 9353 SLIT2 NM_004787 209897_s_at −0.44561465 0.7891295 1841 DTYMK NM_012145 203270_at 0.453199348 0.790596547 9631 NUP155 NM_004298 206550_s_at 0.463044246 0.793503739 5424 POLD1 NM_002691 203422_at 0.436580111 0.79418075 6631 SNRPC NM_003093 201342_at 0.439785378 0.794257849 10186 LHFP NM_005780 218656_s_at −0.45165415 0.800444579 4521 NUDT1 NM_002452 204766_s_at 0.452653404 0.801745536 3479 IGF1 X57025 209540_at −0.44609695 0.802085779 4172 MCM3 NM_002388 201555_at 0.449081552 0.802988628 2205 FCER1A NM_002001 211734_s_at −0.44806141 0.803412984 55732 C1orf112 NM_018186 220840_s_at 0.42605845 0.806117986 9077 DIRAS3 NM_004675 215506_s_at −0.44520841 0.806296741 5557 PRIM1 NM_000946 205053_at 0.449712622 0.807788703 54963 UCKL1 NM_017859 218533_s_at 0.435505247 0.808482789 54512 EXOSC4 NM_019037 218695_at 0.438481818 0.808756437 79901 CYBRD1 Contig52737_RC 217889_s_at −0.44056444 0.809596032 10161 P2RY5 NM_005767 218589_at −0.44050726 0.811708835 29097 CNIH4 NM_014184 218728_s_at 0.405953438 0.816190894 6513 SLC2A1 NM_006516 201250_s_at 0.43835292 0.81712218 51123 ZNF706 NM_016096 218059_at 0.428982832 0.819079758 857 CAV1 NM_001753 203065_s_at −0.42094884 0.825361732 51110 LACTB2 NM_016027 218701_at 0.384063357 0.829135483 51204 CCDC44 NM_016360 221069_s_at 0.414669919 0.829701293 54845 RBM35A NM_017697 219121_s_at 0.404725151 0.831774816 283 ANG NM_001145 205141_at −0.41211819 0.834366082 79652 C16orf30 Contig26371_RC 219315_s_at −0.40614066 0.835774978 56944 OLFML3 NM_020190 218162_at −0.39638017 0.835872435 3297 HSF1 NM_005526 202344_at 0.393113682 0.836172966 27235 COQ2 NM_015697 213379_at 0.394874544 0.838129037 2487 FRZB NM_001463 203698_s_at −0.40214515 0.842301657 3251 HPRT1 NM_000194 202854_at 0.401889944 0.842800545 5119 PCOLN3 NM_002768 201933_at 0.401736559 0.842814242 6839 SUV39H1 NM_003173 218619_s_at 0.396921778 0.845003472 27303 RBMS3 NM_014483 206767_at −0.38281855 0.845114787 10468 FST NM_013409 204948_s_at −0.37734935 0.851436401 26289 AK5 NM_012093 219308_s_at −0.39522360 0.852323896 55038 CDCA4 NM_017955 218399_s_at 0.386970228 0.853046269 7283 TUBG1 NM_001070 201714_at 0.377543673 0.856260137 23212 RRS1 D25218 209567_at 0.381084547 0.859588011 65094 JMJD4 Contig52872_RC 218560_s_at 0.386721791 0.860408119 55379 LRRC59 NM_018509 222231_s_at 0.366371991 0.860584113 10956 NA NM_006812 215399_s_at −0.29552516 0.860849464 51022 GLRX2 NM_016066 219933_at 0.373617007 0.862306014 54915 YTHDF1 NM_017798 221741_s_at 0.367355134 0.86250978 54861 SNRK D43636 209481_at −0.36814557 0.864874681 79000 C1orf135 Contig25124_RC 220011_at 0.34885364 0.865018496 79776 ZFHX4 Contig48790_RC 219779_at −0.37598813 0.866552699 79971 GPR177 Contig53944_RC 221958_s_at −0.34276730 0.866720045 7718 ZNF165 NM_003447 206683_at 0.338079971 0.869974566 201254 STRA13 U95006 209478_at 0.363815143 0.871696996 1848 DUSP6 NM_001946 208893_s_at −0.34350182 0.871975414 9037 SEMA5A NM_003966 205405_at −0.37577719 0.872467328 5433 POLR2D NM_004805 203664_s_at 0.390567073 0.873347886 29087 THYN1 NM_014174 218491_s_at −0.32498531 0.874699946 79864 C11orf63 Contig27559_RC 220141_at −0.35818107 0.875013566 358 AQP1 NM_000385 209047_at −0.32225578 0.876068416 6634 SNRPD3 NM_004175 202567_at 0.356764571 0.876553009 2621 GAS6 NM_000820 202177_at −0.35061025 0.876900397 56270 WDR45L NM_019613 209076_s_at 0.337179642 0.876953353 5187 PER1 NM_002616 202861_at −0.35662350 0.877249218 2098 ESD AF112219 215096_s_at −0.33165654 0.877568889 81887 LAS1L Contig40237_RC 208117_s_at 0.355525467 0.878185905 1811 SLC26A3 NM_000111 206143_at −0.32496995 0.878523665 54535 CCHCR1 NM_019052 42361_g_at 0.303212335 0.879290516 55526 DHTKD1 Contig173 209916_at 0.302461461 0.880741229 57161 PELI2 NM_021255 219132_at −0.34000435 0.881182055 2353 FOS NM_005252 209189_at −0.34853137 0.881316836 51279 C1RL NM_016546 218983_at −0.34801489 0.882609 60436 TGIF2 AF055012 218724_s_at 0.347072353 0.883569866 3028 HSD17B10 NM_004493 202282_at 0.341783943 0.88402224 26519 TIMM10 NM_012456 218408_at 0.342150925 0.884715217 25960 GPR124 AB040964 221814_at −0.33867805 0.88492336 10252 SPRY1 AF041037 212558_at −0.34627190 0.885767923 6199 RPS6KB2 NM_003952 203777_s_at 0.316080366 0.885921604 9824 ARHGAP11A NM_014783 204492_at 0.271468635 0.886970555 55630 SLC39A4 NM_017767 219215_s_at 0.353664658 0.887047277 7049 TGFBR3 NM_003243 204731_at −0.32807103 0.887698816 8607 RUVBL1 NM_003707 201614_s_at 0.268410584 0.888152059 2581 GALC NM_000153 204417_at −0.33728855 0.888213228 862 RUNX1T1 NM_004349 205528_s_at −0.35143858 0.88846914 8458 TTF2 NM_003594 204407_at 0.333371618 0.88848286 9775 EIF4A3 NM_014740 201303_at 0.334470277 0.891654944 3181 HNRPA2B1 NM_002137 205292_s_at 0.334227798 0.892344287 26039 SS18L1 AB014593 213140_s_at 0.31535083 0.892395413 10580 SORBS1 NM_015385 218087_s_at −0.33607143 0.892619568 7056 THBD NM_000361 203888_at −0.30846240 0.894985585 8322 FZD4 NM_012193 218665_at −0.35048586 0.895167871 1003 CDH5 NM_001795 204677_at −0.32733789 0.895661116 2152 F3 NM_001993 204363_at −0.33176999 0.895910725 55068 NA NM_017993 219501_at −0.29959642 0.897626597 64785 GINS3 AL137379 218719_s_at 0.345282183 0.898041826 79042 TSEN34 Contig3597_RC 218132_s_at 0.316134089 0.898125459 8805 TRIM24 NM_015905 204391_x_at 0.320229877 0.899125295 1478 CSTF2 NM_001325 204459_at 0.319509099 0.900149824 1746 DLX2 NM_004405 207147_at −0.32079479 0.902276681 57125 PLXDC1 NM_020405 219700_at −0.27855897 0.902333798 22998 NA AB029025 212328_at −0.31356352 0.903307846 79915 C17orf41 Contig36210_RC 220223_at 0.298348091 0.904268882 7026 NR2F2 M64497 215073_s_at −0.31788442 0.905831798 7474 WNT5A Contig40434_RC 213425_at −0.31039903 0.906409867 55857 C20orf19 NM_018474 219961_s_at −0.33045535 0.90691686 114625 ERMAP NM_018538 219905_at −0.29372548 0.907329798 8857 FCGBP NM_003890 203240_at −0.31144091 0.908506651 26872 STEAP1 NM_012449 205542_at −0.30415820 0.909645834 7226 TRPM2 NM_003307 205708_s_at 0.290916974 0.911329018 29844 TFPT NM_013342 218996_at 0.271529206 0.913433463 4719 NDUFS1 NM_005006 203039_s_at 0.303109253 0.915015151 4013 LOH11CR2A NM_014622 210102_at −0.30279595 0.915117797 3396 ICT1 NM_001545 204868_at 0.292070088 0.91536279 397 ARHGDIB NM_001175 201288_at −0.28431343 0.916109977 10436 EMG1 U72514 209233_at 0.29513303 0.91771301 51582 AZIN1 NM_015878 201772_at 0.28911943 0.917927776 10598 AHSA1 NM_012111 201491_at 0.290857764 0.9179611 333 APLP1 NM_005166 209462_at 0.265203127 0.919016116 51142 CHCHD2 NM_016139 217720_at 0.294292226 0.919415001 27123 DKK2 NM_014421 219908_at −0.28658318 0.919956834 55020 NA NM_017931 218272_at −0.28480702 0.922283445 23460 ABCA6 Contig35210_RC 217504_at −0.27426772 0.922481847 64321 SOX17 Contig37354_RC 219993_at −0.27801934 0.925123949 7098 TLR3 NM_003265 206271_at −0.27152130 0.925325276 6338 SCNN1B NM_000336 205464_at 0.28820584 0.925826366 3692 ITGB4BP NM_002212 210213_s_at 0.263212244 0.926734961 10253 SPRY2 NM_005842 204011_at −0.28525645 0.926765742 2669 GEM NM_005261 204472_at −0.28050966 0.926916522 79679 VTCN1 Contig52970_RC 219768_at −0.26124143 0.927139343 79618 HMBOX1 Contig1982_RC 219269_at −0.27039086 0.92843197 8772 FADD NM_003824 202535_at 0.27301337 0.93042485 9986 RCE1 NM_005133 205333_s_at 0.25749527 0.930511454 58500 ZNF250 X16282 213858_at 0.249529287 0.93097776 11081 KERA NM_007035 220504_at −0.32349270 0.932434909 7064 THOP1 NM_003249 203235_at 0.21439195 0.932738348 55799 CACNA2D3 NM_018398 219714_s_at −0.26160430 0.932985294 49855 ZNF291 AL137612 209741_x_at −0.25994490 0.933064583 54606 DDX56 NM_019082 217754_at 0.202591131 0.934651171 7164 TPD52L1 NM_003287 203786_s_at 0.260470913 0.934685044 80775 TMEM177 Contig49309_RC 218897_at 0.265363587 0.934961966 667 DST NM_001723 204455_at −0.24839799 0.935375903 2781 GNAZ NM_002073 204993_at 0.258872319 0.936532833 23464 GCAT NM_014291 205164_at 0.251880375 0.936847336 79763 ISOC2 Contig2889_RC 218893_at 0.256164207 0.936952189 4649 MYO9A NM_006901 219027_s_at −0.25417332 0.93701735 53820 DSCR6 NM_018962 207267_s_at 0.229254645 0.93734872 3638 INSIG1 NM_005542 201625_s_at 0.284659697 0.938726931 11171 STRAP NM_007178 200870_at 0.252556209 0.940118601 10992 SF3B2 NM_006842 200619_at 0.254492749 0.940473638 6832 SUPV3L1 NM_003171 212894_at 0.253167283 0.940890077 55922 NKRF NM_017544 205004_at 0.237927975 0.9421922 10557 RPP38 NM_006414 205562_at 0.267313355 0.943143623 3216 HOXB6 NM_018952 205366_s_at −0.24536489 0.944854741 54785 C17orf59 NM_017622 219417_s_at −0.23521088 0.945554277 1933 EEF1B2 X60656 200705_s_at −0.23781987 0.945587039 8161 COIL NM_004645 203653_s_at 0.232189669 0.945723554 594 BCKDHB NM_000056 213321_at −0.25979226 0.9475144 6286 S100P NM_005980 204351_at 0.232257446 0.948099124 3954 LETM1 NM_012318 218939_at 0.233460226 0.948276398 51087 YBX2 NM_015982 219704_at 0.196514735 0.948900789 10953 TOMM34 NM_006809 201870_at 0.204607911 0.949034891 PLAU 5328 PLAU NM_002658 211668_s_at 1 0 649 BMP1 NM_001199 207595_s_at 0.686303345 0.534305465 4323 MMP14 NM_004995 202827_s_at 0.666244138 0.559607929 7070 THY1 NM_006288 208850_s_at 0.613593172 0.627698291 1290 COL5A2 NM_000393 221730_at 0.570972856 0.62999627 8038 ADAM12 NM_003474 202952_s_at 0.546163691 0.662574251 23452 ANGPTL2 AF007150 219514_at 0.574017552 0.66386681 4237 MFAP2 NM_017459 203417_at 0.573117712 0.674166716 871 SERPINH1 NM_004353 207714_s_at 0.551607834 0.675286499 1291 COL6A1 X15880 212091_s_at 0.553673759 0.701177797 3671 ISLR NM_005545 207191_s_at 0.513171443 0.726476697 9260 PDLIM7 NM_005451 214121_x_at 0.529257266 0.735614613 55742 PARVA NM_018222 217890_s_at 0.483569524 0.736339664 25903 OLFML2B AL050137 213125_at 0.516201362 0.740220151 6876 TAGLN NM_003186 205547_s_at 0.500057895 0.748828695 5476 CTSA NM_000308 200661_at 0.476318761 0.763036848 5159 PDGFRB NM_002609 202273_at 0.475040267 0.769821276 54587 MXRA8 AL050202 213422_s_at 0.437778456 0.784354172 9180 OSMR NM_003999 205729_at 0.433306368 0.79490084 1281 COL3A1 NM_000090 201852_x_at 0.449280663 0.806105195 26585 GREM1 NM_013372 218468_s_at 0.431076597 0.806133268 2191 FAP NM_004460 209955_s_at 0.449475987 0.808337233 1627 DBN1 NM_004395 217025_s_at 0.429269432 0.809226482 23299 BICD2 AB014599 209203_s_at 0.430848727 0.813994971 51330 TNFRSF12A NM_016639 218368_s_at 0.436061674 0.821259664 7421 VDR NM_000376 204253_s_at 0.423203335 0.823722546 6591 SNAI2 Contig1585_RC 213139_at 0.409857641 0.824381249 2037 EPB41L2 NM_001431 201718_s_at 0.421951551 0.825246889 55033 FKBP14 NM_017946 219390_at 0.425656347 0.827817825 4681 NBL1 NM_005380 201621_at 0.410725353 0.836503012 10487 CAP1 NM_006367 213798_s_at 0.414551349 0.843899961 526 ATP6V1B2 NM_001693 201089_at 0.385305229 0.845387478 2050 EPHB4 NM_004444 216680_s_at 0.33501482 0.850336946 9697 TRAM2 NM_012288 202369_s_at 0.37440913 0.851530018 4921 DDR2 NM_006182 205168_at 0.37934529 0.852102907 9945 GFPT2 NM_005110 205100_at 0.420846996 0.852411188 4811 NID1 NM_002508 202007_at 0.426030363 0.85968909 8481 OFD1 NM_003611 203569_s_at −0.33640817 0.875372065 23705 IGSF4 NM_014333 209030_s_at 0.326615812 0.877277896 23166 STAB1 AJ275213 204150_at 0.345752035 0.879137539 8459 TPST2 NM_003595 204079_at 0.292694524 0.879236195 23645 PPP1R15A NM_014330 202014_at 0.334435453 0.88314905 27295 PDLIM3 NM_014476 209621_s_at 0.344670867 0.885652512 93974 ATPIF1 NM_016311 218671_s_at −0.32802985 0.886105389 51592 TRIM33 NM_015906 212435_at −0.33038360 0.895125804 4314 MMP3 NM_002422 205828_at 0.304242677 0.895658603 1833 EPYC NM_004950 206439_at 0.337308341 0.895915378 157567 ANKRD46 U79297 212731_at −0.32344971 0.898025232 8904 CPNE1 NM_003915 206918_s_at 0.318038406 0.900793856 602 BCL3 NM_005178 204907_s_at 0.304998235 0.904399401 2720 GLB1 NM_000404 201576_s_at 0.322062138 0.906764094 59286 UBL5 Contig65670_RC 218011_at −0.27021325 0.914865462 8408 ULK1 NM_003565 209333_at 0.27421269 0.918353875 55035 NOL8 NM_017948 218244_at −0.27456644 0.922310693 7042 TGFB2 NM_003238 220407_s_at 0.286360255 0.923466436 5155 PDGFB NM_002608 204200_s_at 0.269055708 0.931600028 10409 BASP1 NM_006317 202391_at 0.244062133 0.932183339 10993 SDS NM_006843 205695_at 0.245388394 0.933091037 6233 RPS27A NM_002954 200017_at −0.26468902 0.933902258 8507 ENC1 NM_003633 201340_s_at 0.230967436 0.934843627 176 AGC1 NM_013227 217161_x_at 0.214527206 0.938418486 9849 ZNF518 NM_014803 204291_at −0.27940542 0.941723169 51463 GPR89A NM_016334 222140_s_at −0.24633996 0.942684028 6141 RPL18 NM_000979 222297_x_at −0.24477092 0.944074771 4205 MEF2A NM_005587 208328_s_at 0.206794876 0.9444056 1774 DNASE1L1 NM_006730 203912_s_at 0.232623402 0.946207309 4430 MYO1B AK000160 212364_at 0.228075133 0.947362794 57158 JPH2 NM_020433 220385_at 0.163350482 0.949439143 VEGF 7422 VEGFA NM_003376 211527_x_at 1 0 911 CD1C NM_001765 205987_at −0.30279189 0.875335287 4005 LMO2 NM_005574 204249_s_at −0.35419700 0.876731359 4222 MEOX1 NM_013999 205619_s_at −0.35048957 0.882751646 29927 SEC61A1 NM_013336 217716_s_at 0.348075751 0.885518246 6166 RPL36AL NM_001001 207585_s_at −0.33751206 0.887065036 9450 LY86 NM_004271 205859_at −0.29401754 0.907178982 22900 CARD8 NM_014959 204950_at −0.29984162 0.912490569 1776 DNASE1L3 NM_004944 205554_s_at −0.29876991 0.915582301 1119 CHKA NM_001277 204233_s_at 0.293232546 0.918063311 22809 ATF5 NM_012068 204999_s_at 0.217042464 0.937083889 23417 MLYCD NM_012213 218869_at −0.23534131 0.939494944 23592 LEMD3 NM_014319 218604_at −0.26982318 0.947647276 51621 KLF13 NM_015995 219878_s_at 0.242003861 0.947879938 STAT1 6772 STAT1 NM_007315 209969_s_at 1 0 3627 CXCL10 NM_001565 204533_at 0.791673192 0.373734657 6890 TAP1 NM_000593 202307_s_at 0.773730642 0.38014378 6373 CXCL11 NM_005409 210163_at 0.729976561 0.469038038 3620 INDO NM_002164 210029_at 0.693332241 0.480540278 4283 CXCL9 NM_002416 203915_at 0.705931141 0.506582671 4599 MX1 NM_002462 202086_at 0.700341707 0.512026803 27074 LAMP3 NM_014398 205569_at 0.691286706 0.51665141 9636 ISG15 NM_005101 205483_s_at 0.692921839 0.521514816 64108 RTP4 Contig51660_RC 219684_at 0.66510774 0.521724062 55008 HERC6 NM_017912 219352_at 0.680045765 0.534540502 10964 IFI44L NM_006820 204439_at 0.68441612 0.53484654 4600 MX2 M30818 204994_at 0.676333667 0.545187222 3437 IFIT3 NM_001549 204747_at 0.676843523 0.547342002 51191 HERC5 NM_016323 219863_at 0.654162297 0.55158659 91543 RSAD2 AF026941 213797_at 0.654314865 0.566762715 23586 DDX58 NM_014314 218943_s_at 0.640872007 0.568844077 6352 CCL5 NM_002985 1405_i_at 0.660200416 0.568867672 27299 ADAMDEC1 NM_014479 206134_at 0.642299127 0.589527746 914 CD2 NM_001767 205831_at 0.644301271 0.616877785 55601 NA NM_017631 218986_s_at 0.613852226 0.621928407 10866 HCP5 NM_006674 206082_at 0.610103583 0.629169819 9111 NMI NM_004688 203964_at 0.603257958 0.639437655 9806 SPOCK2 NM_014767 202524_s_at 0.584098575 0.641216629 6355 CCL8 NM_005623 214038_at 0.570756407 0.651950505 10346 TRIM22 NM_006074 213293_s_at 0.590810894 0.652849087 4069 LYZ NM_000239 213975_s_at 0.544927822 0.662182124 3659 IRF1 NM_002198 202531_at 0.589919529 0.66222688 3902 LAG3 NM_002286 206486_at 0.541977347 0.668358145 9595 PSCDBP NM_004288 209606_at 0.567980838 0.668469879 22797 TFEC NM_012252 206715_at 0.599293976 0.668483201 10537 UBD NM_006398 205890_s_at 0.578544702 0.670772877 11262 SP140 NM_007237 207777_s_at 0.577805009 0.679232612 1075 CTSC NM_001814 201487_at 0.562320779 0.681366545 2537 IFI6 NM_002038 204415_at 0.563222465 0.683899859 7941 PLA2G7 NM_005084 206214_at 0.557200093 0.695642543 917 CD3G NM_000073 206804_at 0.55769671 0.698961356 1890 ECGF1 NM_001953 204858_s_at 0.546473637 0.700870238 51316 PLAC8 NM_016619 219014_at 0.538438452 0.703113148 10875 FGL2 NM_006682 204834_at 0.524540085 0.705303623 3003 GZMK NM_002104 206666_at 0.530074132 0.717735405 962 CD48 NM_001778 204118_at 0.533233612 0.719024509 6775 STAT4 NM_003151 206118_at 0.550392357 0.72324098 2841 GPR18 Contig35647_RC 210279_at 0.521231488 0.726949329 5026 P2RX5 NM_002561 210448_s_at 0.504830283 0.729589032 10437 IFI30 NM_006332 201422_at 0.511822231 0.735812254 4068 SH2D1A NM_002351 210116_at 0.471245594 0.7433416 7805 LAPTM5 NM_006762 201720_s_at 0.498421145 0.746819193 969 CD69 NM_001781 209795_at 0.471158768 0.753189587 5778 PTPN7 NM_002832 204852_s_at 0.499057802 0.75677133 3394 IRF8 NM_002163 204057_at 0.489162341 0.768389511 11040 PIM2 NM_006875 204269_at 0.47698737 0.770321793 51513 ETV7 NM_016135 221680_s_at 0.532716749 0.771749503 29909 GPR171 NM_013308 207651_at 0.467045116 0.776788947 5720 PSME1 NM_006263 200814_at 0.463856614 0.778162143 330 BIRC3 NM_001165 210538_s_at 0.47318545 0.778456521 356 FASLG NM_000639 210865_at 0.521488064 0.782352474 8519 IFITM1 NM_003641 201601_x_at 0.469088027 0.78238098 24138 IFIT5 NM_012420 203596_s_at 0.466667589 0.783188342 3689 ITGB2 NM_000211 202803_s_at 0.461692343 0.784532984 11118 BTN3A2 NM_007047 212613_at 0.461680236 0.788500748 3059 HCLS1 NM_005335 202957_at 0.450361209 0.795023723 6398 SECTM1 NM_003004 213716_s_at 0.425961617 0.799831467 55843 ARHGAP15 NM_018460 218870_at 0.417535994 0.801382989 22914 KLRK1 NM_007360 205821_at 0.437660493 0.809727352 10261 IGSF6 NM_005849 206420_at 0.436549677 0.81219172 1880 EBI2 NM_004951 205419_at 0.399159019 0.815726925 26034 NA AB007863 214735_at 0.40937931 0.829560298 29887 SNX10 NM_013322 218404_at 0.400589724 0.835603896 79132 NA Contig63102_RC 219364_at 0.391375097 0.849609415 684 BST2 NM_004335 201641_at 0.384303271 0.854129545 55337 NA NM_018381 218429_s_at 0.386327296 0.857355054 341 APOC1 NM_001645 204416_x_at 0.36462583 0.861296021 51237 NA NM_016459 221286_s_at 0.370554593 0.874957917 445347 NA M17323 209813_x_at 0.305107684 0.886124869 56829 ZC3HAV1 NM_020119 220104_at 0.342023355 0.888935417 23564 DDAH2 NM_013974 214909_s_at −0.33358568 0.889200466 23547 LILRA4 AF041261 210313_at 0.341444621 0.894341374 10148 EBI3 NM_005755 219424_at 0.284618325 0.894479773 3823 KLRC3 NM_007333 207723_s_at 0.269791167 0.896638494 50856 CLEC4A NM_016184 221724_s_at 0.348085505 0.90159803 959 CD40LG NM_000074 207892_at 0.330319064 0.90731366 7409 VAV1 NM_005428 206219_s_at 0.346468277 0.907387687 2745 GLRX NM_002064 206662_at 0.30616967 0.910310197 54 ACP5 NM_001611 204638_at 0.276526368 0.911099185 5993 RFX5 NM_000449 202964_s_at 0.292677164 0.911410075 51816 CECR1 NM_017424 219505_at 0.305675892 0.913657631 7187 TRAF3 NM_003300 208315_x_at 0.246604319 0.921975101 4218 RAB8A NM_005370 208819_at 0.272692263 0.923395016 3606 IL18 NM_001562 206295_at 0.265963985 0.927706943 1942 EFNA1 NM_004428 202023_at −0.25887098 0.934754499 10125 RASGRP1 NM_005739 205590_at 0.256021016 0.936422237 9985 REC8L1 NM_005132 218599_at 0.258614123 0.936428333 9034 CCRL2 NM_003965 211434_s_at 0.318651272 0.940353226 10126 DNAL4 NM_005740 204008_at −0.21990042 0.943877702 CASP3 836 CASP3 NM_004346 202763_at 1 0 10393 ANAPC10 NM_014885 207845_s_at 0.356889908 0.902909966 7738 ZNF184 U66561 213452_at 0.2920488 0.913630754 3728 JUP NM_002230 201015_s_at −0.27257126 0.924223529 8237 USP11 NM_004651 208723_at −0.29065181 0.925692835 402 ARL2 NM_001667 202564_x_at −0.25533419 0.935253954 25978 CHMP2B NM_014043 202536_at 0.265905131 0.937256343 6301 SARS NM_006513 200802_at −0.25179738 0.937862493 55361 NA AL353952 209346_s_at −0.24294692 0.943220971 5977 DPF2 NM_006268 202116_at −0.21593926 0.947438324

TABLE 10 gene.symbol EntrezGene.ID ALPI 248 ANPEP 290 ARHGDIB 397 BAG4 9530 BAX 581 BBS9 27241 BID 637 BIRC3 330 BLVRA 644 C17orf46 124783 CASP10 843 CASP6 839 CASP8 841 CASP9 842 CD28 940 CD33 945 CD4 920 CD40 958 CD44 960 CD5 921 CD7 924 CD80 941 CD86 942 CFLAR 8837 CR2 1380 CRADD 8738 CSNK1D 1453 CUTL1 1523 CYCS 54205 DAXX 1616 EIF4A1 1973 EIF4E 1977 ELK1 2002 FAF1 11124 FAS 355 FKBP1A 2280 GRB2 2885 HLA-A 3105 HLA-DRB1 3123 HLA-DRB5 3127 ICAM1 3383 ICOSLG 23308 IKBKB 3551 IL10RA 3587 IL12B 3593 IL12RB2 3595 IL13 3596 IL15 3600 IL1A 3552 IL2RA 3559 IL3 3562 IL4R 3566 IRAK2 3656 ITGA4 3676 ITGAM 3684 ITGAX 3687 ITK 3702 JAK1 3716 JAK3 3718 JUNB 3726 LMNA 4000 LMNB1 4001 LTA 4049 MADD 8567 MAF 4094 MAP2K3 5606 MAP3K14 9020 MAP3K7IP1 10454 MAP4K2 5871 MAPK1 5594 MAPK8 5599 MYD88 4615 NCF2 4688 NFKB1 4790 NR3C1 2908 NSMAF 8439 PAK2 5062 PDK2 5164 PIK3C2G 5288 PLCB1 23236 PPP1R13B 23368 PPP3CA 5530 PRF1 5551 PRKAR1B 5575 PRKDC 5591 PTEN 5728 PTENP1 11191 PTPRC 5788 PVRL1 5818 RAF1 5894 RELA 5970 RHEB 6009 RPS6KB1 6198 SPTAN1 6709 STAT3 6774 STAT5A 6776 TANK 10010 TAP1 6890 TAP2 6891 TGFB1 7040 TNF 7124 TNFRSF10A 8797 TNFRSF13B 23495 TNFRSF1B 7133 TNFRSF25 8718 TNFSF13B 10673 TOLLIP 54472 TRA@ 6955 TRAF1 7185 TRAF3 7187

TABLE 11 gene.symbol EntrezGene.ID ACP5 54 ADAMDEC1 27299 APOC1 341 ARHGAP15 55843 BIRC3 330 BST2 684 BTN3A2 11118 CCL5 6352 CCL8 6355 CCRL2 9034 CD2 914 CD3G 917 CD40LG 959 CD48 962 CD69 969 CECR1 51816 CLEC4A 50856 CTSC 1075 CXCL10 3627 CXCL11 6373 CXCL9 4283 DDAH2 23564 DDX58 23586 DNAL4 10126 EBI2 1880 EBI3 10148 ECGF1 1890 EFNA1 1942 ETV7 51513 FASLG 356 FGL2 10875 FLJ11286 55337 FLJ20035 55601 GLRX 2745 GPR171 29909 GPR18 2841 GZMK 3003 HCLS1 3059 HCP5 10866 HERC5 51191 HERC6 55008 IFI30 10437 IFI44L 10964 IFI6 2537 IFIT3 3437 IFIT5 24138 IFITM1 8519 IGSF6 10261 IL18 3606 INDO 3620 IRF1 3659 IRF8 3394 ISG15 9636 ITGB2 3689 KLRC3 3823 KLRK1 22914 LAG3 3902 LAMP3 27074 LAPTM5 7805 LGP2 79132 LILRA4 23547 LILRB1 10859 MGC29506 51237 MX1 4599 MX2 4600 NMI 9111 P2RX5 5026 PIM2 11040 PIP3-E 26034 PLA2G7 7941 PLAC8 51316 PSCDBP 9595 PSME1 5720 PTPN7 5778 RAB8A 4218 RASGRP1 10125 REC8L1 9985 RFX5 5993 RSAD2 91543 RTP4 64108 SECTM1 6398 SH2D1A 4068 SNX10 29887 SP140 11262 SPOCK2 9806 STAT1 6772 STAT4 6775 TAP1 6890 TFEC 22797 TRAF3 7187 TRGV9 6983 TRIM22 10346 UBD 10537 VAV1 7409 ZC3HAV1 56829

TABLE 12 gene.symbol EntrezGene.ID FGD6 55785 PLAC9 219348 CAB39L 81617 FGD6 55785 LONRF3 79836 CGI-38 51673 STXBP6 29091 FHL1 2273 STXBP6 29091 LEPR 3953 CA4 762 TNMD 64102 POSTN 10631 LOC58489 58489 LOC284825 284825 LRP1B 53353 TIMP4 7079 STXBP6 29091 WNT11 7481 PLAC9 219348 MICAL2 9645 PKD1L2 114780 SDC1 6382 FHL1 2273 FHL1 2273 F2RL2 2151 AKR1C2 1646 LEF1 51176 ADAM12 8038 ADH1C 126 VIT 5212 HOP 84525 GPX3 2878 RRM2 6241 GPX3 2878 MYOC 4653 CLEC3B 7123 GRP 2922 GJB2 2706 AADAC 13 MATN3 4148 PPAPDC1A 196051 LOC646324 646324 COL10A1 1300 COL10A1 1300

TABLE 13 gene.symbol EntrezGene.ID PLAU 5328 BMP1 649 MMP14 4323 THY1 7070 COL5A2 1290 ADAM12 8038 ANGPTL2 23452 MFAP2 4237 SERPINH1 871 COL6A1 1291 ISLR 3671 PDLIM7 9260 PARVA 55742 OLFML2B 25903 TAGLN 6876 CTSA 5476 PDGFRB 5159 MXRA8 54587 OSMR 9180 COL3A1 1281 GREM1 26585 FAP 2191 DBN1 1627 BICD2 23299 TNFRSF12A 51330 VDR 7421 SNAI2 6591 EPB41L2 2037 FKBP14 55033 NBL1 4681 CAP1 10487 ATP6V1B2 526 EPHB4 2050 TRAM2 9697 DDR2 4921 GFPT2 9945 NID1 4811 OFD1 8481 CADM1 23705 STAB1 23166 TPST2 8459 PPP1R15A 23645 PDLIM3 27295 ATPIF1 93974 TRIM33 51592 MMP3 4314 EPYC 1833 ANKRD46 157567 CPNE1 8904 BCL3 602 GLB1 2720 UBL5 59286 ULK1 8408 NOL8 55035 TGFB2 7042 PDGFB 5155 BASP1 10409 SDS 10993 RPS27A 6233 ENC1 8507 ACAN 176 ZNF518 9849 GPR89A 51463 RPL18 6141 MEF2A 4205 DNASE1L1 1774 MYO1B 4430 JPH2 57158

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1. A gene or protein set consisting of at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, and possibly 40, 45, 50, 55, 60, 65 genes or proteins or the entire set selected from the table 12 and/or the table 13, antibodies or hypervariable portion thereof directed against the proteins encoded by these genes.
 2. The gene or protein set according to claim 1, wherein the gene or proteins sequences or the antibodies are bound to a solid support surface, such as an array.
 3. Diagnostic kit or device comprising the gene or protein set according to claim 1 and other means for real time PCR analysis or protein analysis.
 4. The kit or device according to claim 3, wherein the means for real time PCR are means for qRT-PCR.
 5. The kit or device according to claim 3, which further comprises a gene or protein set consisting of at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95 possibly 100, 105, 110 genes or proteins or the entire set selected from the table 10 and/or the table 11, antibodies or hypervariable portion thereof directed against the proteins encoded by these genes.
 6. The kit or device according to claim 3, which further comprises a gene or protein set consisting of at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 55, 60, 65, 70, 75, 80, 85, 90 or 95 genes or proteins or the entire set designated as upregulated genes/proteins in grade 3 tumor in ER+ patients in the table 3 of the document WO 2006/119593 antibodies or hypervariable portion thereof directed against the proteins encoded by these genes.
 7. The kit or device according to the claim 6, wherein the genes are proliferation relating genes, preferably selected from the group consisting of CCNB1, CCNA2, CDC2, CDC20, MCM2, MYBL2, KPNA2 and STK6.
 8. The kit or device according to claim 3, which further comprises one or more reference genes selected from the group consisting of TFRC, GUS, RPLPO and TBP.
 9. The kit or device according to claim 1 comprising a computerized system comprising a bio-assay module configured for detecting a gene expression or protein analysis from a tumor sample based upon the gene or protein set according to claim 1 and a processor module configured to calculate expression of the gene or the protein synthesis and to generate a risk assessment for the tumor sample.
 10. The kit or device according to the claim 9, wherein the tumor sample is a breast tumor sample.
 11. A gene or protein set consisting of at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 55, 60, 65, 70, 75, 80, 85, 90 or 95 or proteins or the entire set selected from the table 11 and/or the table 13 or antibodies or hypervariable portion thereof directed against the proteins encoded by these genes.
 12. A method for a prognosis (prognostic) of cancer in mammal subject, which comprises the step of collecting a tumor sample, preferably a breast tumor sample, from the mammal subject and measuring gene expression in the tumor sample by putting and measuring gene expression or protein synthesis in the tumor sample by putting into contact nucleotide and/or amino acids sequences obtained from this tumor sample with the gene or protein set of claim 1 and generating a risk assessment for the tumor sample as different subtypes within ER− type and within HER2+ and/or ER+ types.
 13. A gene or protein set comprising at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, and possibly 40, 45, 50, 55, 60, 65 genes or proteins or the entire set selected from the table 12 and/or the table 13, antibodies or hypervariable portion thereof directed against the proteins encoded by these genes.
 14. The kit or device according to claim 3, which further comprises a gene or protein set comprising at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95 possibly 100, 105, 110 genes or proteins or the entire set selected from the table 10 and/or the table 11, antibodies or hypervariable portion thereof directed against the proteins encoded by these genes.
 15. The kit or device according to claim 3, which further comprises a gene or protein set comprising at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 55, 60, 65, 70, 75, 80, 85, 90 or 95 genes or proteins or the entire set designated as upregulated genes/proteins in grade 3 tumor in ER+ patients in the table 3 of the document WO 2006/119593 antibodies or hypervariable portion thereof directed against the proteins encoded by these genes.
 16. The kit or device according to claim 6, wherein the genes are proliferation relating genes, preferably selected from the group consisting of the gene CDC2, CDC20, MYBL2 and KPNA2.
 17. A method for a prognosis (prognostic) of cancer in mammal subject according to claim 12 wherein the subject comprises an ER− human patient. 